Triple
T15447309
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jean Graczyk |
E370056
|
entity |
| Predicate | burialPlace |
P196
|
FINISHED |
| Object | Vierzon |
E295861
|
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: Vierzon | Statement: [Jean Graczyk, burialPlace, Vierzon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vierzon Context triple: [Jean Graczyk, burialPlace, Vierzon]
-
A.
Vierzon
chosen
Vierzon is a town in central France known historically as an industrial and railway hub in the Cher department of the Centre-Val de Loire region.
-
B.
Tournus
Tournus is a historic town in eastern France’s Burgundy region, known for its Romanesque abbey and riverside setting along the Saône.
-
C.
Soissons
Soissons is a historic town in northern France known for its strategic military importance and notable battles throughout European history.
-
D.
Saint-Dizier
Saint-Dizier is a commune in northeastern France known as an industrial town in the Haute-Marne department of the Grand Est region.
-
E.
Lubersac
Lubersac is a small commune in the Corrèze department of south-central France, known for its rural character and traditional Limousin heritage.
- 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_69d85a19180081909925012fbf4e62a3 |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03ef767b4819099f2c0919a158321 |
completed | April 16, 2026, 1:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0170d8c9f0819099a398814f49f0ed |
completed | May 11, 2026, 6:02 a.m. |
Created at: April 10, 2026, 3:21 a.m.