Triple
T7760873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | House of Lorraine |
E176016
|
entity |
| Predicate | hasMainTitle |
P20947
|
FINISHED |
| Object |
Count of Marsan
The Count of Marsan was a noble title historically associated with a cadet branch of the influential House of Lorraine in France.
|
E686944
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Count of Marsan | Statement: [House of Lorraine, hasMainTitle, Count of Marsan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Count of Marsan Context triple: [House of Lorraine, hasMainTitle, Count of Marsan]
-
A.
Marsan
Marsan is a small rural settlement located within the Qakh District of Azerbaijan.
-
B.
Mars of Todi
Mars of Todi is a renowned life-size Etruscan bronze statue of a warrior, notable for its detailed armor and blend of Etruscan and classical Greek artistic influences.
-
C.
Mars Gradivus
Mars Gradivus is a martial aspect of the Roman god Mars, venerated as a fierce, battle-ready deity who strides into war at the head of armies.
-
D.
Marsden
Marsden is a village in West Yorkshire, England, situated in the Colne Valley near the Pennines and known for its industrial heritage and scenic moorland surroundings.
-
E.
MARS
MARS is a prominent benchmark suite used in computer architecture and systems research, often serving as a standard workload for evaluating and comparing processor and memory system performance.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Count of Marsan Triple: [House of Lorraine, hasMainTitle, Count of Marsan]
Generated description
The Count of Marsan was a noble title historically associated with a cadet branch of the influential House of Lorraine in France.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Count of Marsan Target entity description: The Count of Marsan was a noble title historically associated with a cadet branch of the influential House of Lorraine in France.
-
A.
Marsan
Marsan is a small rural settlement located within the Qakh District of Azerbaijan.
-
B.
Mars of Todi
Mars of Todi is a renowned life-size Etruscan bronze statue of a warrior, notable for its detailed armor and blend of Etruscan and classical Greek artistic influences.
-
C.
Mars Gradivus
Mars Gradivus is a martial aspect of the Roman god Mars, venerated as a fierce, battle-ready deity who strides into war at the head of armies.
-
D.
Marsden
Marsden is a village in West Yorkshire, England, situated in the Colne Valley near the Pennines and known for its industrial heritage and scenic moorland surroundings.
-
E.
MARS
MARS is a prominent benchmark suite used in computer architecture and systems research, often serving as a standard workload for evaluating and comparing processor and memory system performance.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69c69962923c8190ac74d28b4f9fe0a0 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c704036c588190a441e56c738cc309 |
completed | March 27, 2026, 10:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8c7d2be488190bad1026b76fd0cd3 |
completed | March 29, 2026, 6:33 a.m. |
| NEDg | Description generation | batch_69c8c855af8881908bad7f278c492877 |
completed | March 29, 2026, 6:36 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8c8c516048190957d937f2f2273ad |
completed | March 29, 2026, 6:37 a.m. |
Created at: March 27, 2026, 4:09 p.m.