Triple
T15284245
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gonzaga |
E365353
|
entity |
| Predicate | regionRuled |
P15936
|
FINISHED |
| Object | Nevers |
E172114
|
NE FINISHED |
How this triple was built (2 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: Nevers | Statement: [Gonzaga, regionRuled, Nevers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nevers Context triple: [Gonzaga, regionRuled, Nevers]
-
A.
Nevers
chosen
Nevers is a historic city in central France known for its medieval architecture, religious heritage, and traditional faience pottery.
-
B.
Villeblevin
Villeblevin is a small commune in north-central France, best known as the place where writer Albert Camus died in a car accident.
-
C.
Toul
Toul is a historic commune in northeastern France known for its medieval fortifications and impressive Gothic cathedral.
-
D.
Noville
Noville is a small Swiss municipality in the canton of Vaud, situated near the eastern end of Lake Geneva and known for its natural wetlands and rural character.
-
E.
Villeurbannais
Villeurbannais is the French term for an inhabitant or native of the city of Villeurbanne, located near Lyon in eastern France.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d85a103d9081908c1ea6c4c73ac8e3 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00e53c9588190a6cb61ac8805c706 |
completed | April 15, 2026, 10:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feef798a588190981c77e6f4c6be78 |
completed | May 9, 2026, 8:25 a.m. |
Created at: April 10, 2026, 3:15 a.m.