Triple
T1073926
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ferdinand Foch |
E23389
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Tarbes |
E109577
|
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: Tarbes | Statement: [Ferdinand Foch, placeOfBirth, Tarbes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tarbes Context triple: [Ferdinand Foch, placeOfBirth, Tarbes]
-
A.
Tarbes
chosen
Tarbes is a historic city in southwestern France, serving as the capital of the Hautes-Pyrénées department at the foot of the Pyrenees.
-
B.
Bagnères-de-Bigorre
Bagnères-de-Bigorre is a spa and ski resort town in the French Pyrenees, known for its thermal baths and mountain tourism.
-
C.
Narbonne
Narbonne is a historic city in southern France known for its Roman heritage, medieval architecture, and former status as an important Mediterranean port.
-
D.
Montauban
Montauban is a historic city in southern France known for its red-brick architecture and role as the capital of the Tarn-et-Garonne department.
-
E.
Margat
Margat is a prominent medieval Crusader fortress in Syria that served as a major stronghold of the Knights Hospitaller.
- 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_69a493ee1f908190992b5f0d1b04459b |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b92cbfd481909e2f928c1d06ebaa |
completed | March 1, 2026, 10:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acc60b052c8190bacb19f0fdc8059a |
completed | March 8, 2026, 12:42 a.m. |
Created at: March 1, 2026, 7:42 p.m.