Triple
T3135792
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pierre Bérégovoy |
E65525
|
entity |
| Predicate | placeOfDeath |
P21
|
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: [Pierre Bérégovoy, placeOfDeath, Nevers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nevers Context triple: [Pierre Bérégovoy, placeOfDeath, Nevers]
-
A.
Nevers
chosen
Nevers is a historic city in central France known for its medieval architecture, religious heritage, and traditional faience pottery.
-
B.
Trois-Ponts
Trois-Ponts is a small municipality in the province of Liège in Wallonia, Belgium, known for its scenic Ardennes landscape and railway junction.
-
C.
Houilles
Houilles is a suburban commune in north-central France, located in the western outskirts of Paris within the Yvelines department.
-
D.
Saverne
Saverne is a historic town in northeastern France, known for its canal, rose gardens, and the Château des Rohan.
-
E.
Nanterre
Nanterre is a western suburb of Paris in the Hauts-de-Seine department of France, known as an important administrative and educational center.
- 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_69ad8581c25c8190b0d85ba9b9baa531 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada5637de0819089393429c4017298 |
completed | March 8, 2026, 4:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b20f8793488190aa31040edaf1d627 |
completed | March 12, 2026, 12:57 a.m. |
Created at: March 8, 2026, 3:05 p.m.