Triple
T15718324
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | House of Limburg |
E381017
|
entity |
| Predicate | heldTitle |
P8
|
FINISHED |
| Object |
Count of Arlon
Count of Arlon was a medieval noble title associated with the region around Arlon in present-day Belgium, historically linked to the House of Limburg.
|
E1172812
|
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 Arlon | Statement: [House of Limburg, heldTitle, Count of Arlon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Count of Arlon Context triple: [House of Limburg, heldTitle, Count of Arlon]
-
A.
Gembloux
Gembloux is a town in the Walloon region of Belgium known for its historic center and agricultural university.
-
B.
Casteau
Casteau is a village in Belgium best known as the site of NATO’s Supreme Headquarters Allied Powers Europe (SHAPE).
-
C.
Count of Saint-Leu
The Count of Saint-Leu was a noble title held by Louis Bonaparte, former King of Holland and younger brother of Napoleon Bonaparte.
-
D.
Fernelmont
Fernelmont is a rural municipality in the province of Namur in Wallonia, Belgium, known for its agricultural landscape and small villages.
-
E.
Morangis
Morangis is a commune in the southern suburbs of Paris, located in the Essonne department in the Île-de-France region of northern France.
- 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 Arlon Triple: [House of Limburg, heldTitle, Count of Arlon]
Generated description
Count of Arlon was a medieval noble title associated with the region around Arlon in present-day Belgium, historically linked to the House of Limburg.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Count of Arlon Target entity description: Count of Arlon was a medieval noble title associated with the region around Arlon in present-day Belgium, historically linked to the House of Limburg.
-
A.
Gembloux
Gembloux is a town in the Walloon region of Belgium known for its historic center and agricultural university.
-
B.
Casteau
Casteau is a village in Belgium best known as the site of NATO’s Supreme Headquarters Allied Powers Europe (SHAPE).
-
C.
Count of Saint-Leu
The Count of Saint-Leu was a noble title held by Louis Bonaparte, former King of Holland and younger brother of Napoleon Bonaparte.
-
D.
Fernelmont
Fernelmont is a rural municipality in the province of Namur in Wallonia, Belgium, known for its agricultural landscape and small villages.
-
E.
Morangis
Morangis is a commune in the southern suburbs of Paris, located in the Essonne department in the Île-de-France region of northern France.
- 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_69d86d9bf930819082b30cf6d169297c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04f932a248190b65ecfb2bc56e715 |
completed | April 16, 2026, 2:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff7583609c8190a80421fce649900f |
completed | May 9, 2026, 5:57 p.m. |
| NEDg | Description generation | batch_69ff76deb1948190bc49825719ac97d5 |
completed | May 9, 2026, 6:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff77642ba4819095c1acc65da06135 |
completed | May 9, 2026, 6:05 p.m. |
Created at: April 10, 2026, 4:45 a.m.