Triple
T4498359
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bigorre |
E100753
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Armagnac |
E206235
|
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: Armagnac | Statement: [Bigorre, borders, Armagnac]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Armagnac Context triple: [Bigorre, borders, Armagnac]
-
A.
Armagnac
chosen
Armagnac is a historic French brandy-producing region renowned for its distinctive, long-aged eaux-de-vie made from local grapes.
-
B.
Cognac
Cognac is a renowned French town in the Charente department, famous worldwide as the center of production for the eponymous brandy.
-
C.
Calvados
Calvados is a department in the Normandy region of northwestern France, known for its historic D-Day landing beaches and production of the apple brandy that shares its name.
-
D.
Hennessy
Hennessy is a surname most prominently associated with John L. Hennessy, a renowned computer scientist and former president of Stanford University.
-
E.
Hennessy
Hennessy is a world-renowned French cognac producer, recognized as one of the leading and most prestigious brands in the global spirits industry.
- 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_69bd43cdf15081909a4fa2585ff63b3e |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd56c065e88190934eb0b1632d79bb |
completed | March 20, 2026, 2:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bd6f850824819092e518e1bd950f80 |
completed | March 20, 2026, 4:02 p.m. |
Created at: March 20, 2026, 1 p.m.