Triple
T680137
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gardon River |
E13163
|
entity |
| Predicate | locatedInDepartment |
P40
|
FINISHED |
| Object |
Gard
Gard is a department in southern France known for its Mediterranean landscapes, historic towns, and the famous Pont du Gard Roman aqueduct.
|
E89752
|
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: Gard | Statement: [Gardon River, locatedInDepartment, Gard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gard Context triple: [Gardon River, locatedInDepartment, Gard]
-
A.
Lübars
Lübars is a historic, village-like district in Berlin’s Reinickendorf borough, known for its rural character, fields, and preserved traditional architecture within the city.
-
B.
Tivoli
Tivoli is an Italian hill town east of Rome renowned for its historic villas and gardens, including Emperor Hadrian’s vast imperial retreat, Hadrian’s Villa.
-
C.
Heden
Heden is a central district in Gothenburg, Sweden, known for its sports facilities, event venues, and open recreational spaces.
-
D.
Groves
Groves is a surname most notably associated with U.S. Army General Leslie R. Groves Jr., who directed the Manhattan Project during World War II.
-
E.
Arendal
Arendal is a coastal town and municipality in southern Norway known historically as a regional political and trading center.
- 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: Gard Triple: [Gardon River, locatedInDepartment, Gard]
Generated description
Gard is a department in southern France known for its Mediterranean landscapes, historic towns, and the famous Pont du Gard Roman aqueduct.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gard Target entity description: Gard is a department in southern France known for its Mediterranean landscapes, historic towns, and the famous Pont du Gard Roman aqueduct.
-
A.
Lübars
Lübars is a historic, village-like district in Berlin’s Reinickendorf borough, known for its rural character, fields, and preserved traditional architecture within the city.
-
B.
Tivoli
Tivoli is an Italian hill town east of Rome renowned for its historic villas and gardens, including Emperor Hadrian’s vast imperial retreat, Hadrian’s Villa.
-
C.
Heden
Heden is a central district in Gothenburg, Sweden, known for its sports facilities, event venues, and open recreational spaces.
-
D.
Groves
Groves is a surname most notably associated with U.S. Army General Leslie R. Groves Jr., who directed the Manhattan Project during World War II.
-
E.
Arendal
Arendal is a coastal town and municipality in southern Norway known historically as a regional political and trading center.
- 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_69a4933d3bf88190972041cd8cf143b9 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a04f4efc819082767a7517fa760a |
completed | March 1, 2026, 8:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a65e3575388190a674df54e086fe2f |
completed | March 3, 2026, 4:06 a.m. |
| NEDg | Description generation | batch_69a65e9a20748190b499182db9fc8cbb |
completed | March 3, 2026, 4:07 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a65f13d140819086042d9b21f842f8 |
completed | March 3, 2026, 4:09 a.m. |
Created at: March 1, 2026, 7:36 p.m.