Triple
T17123031
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aquitaine |
E415514
|
entity |
| Predicate | importantMedievalCity |
P98393
|
FINISHED |
| Object | Périgueux |
E287272
|
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: Périgueux | Statement: [Aquitaine, importantMedievalCity, Périgueux]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Périgueux Context triple: [Aquitaine, importantMedievalCity, Périgueux]
-
A.
Périgueux
chosen
Périgueux is a historic city in southwestern France known for its well-preserved medieval and Renaissance architecture and its rich Gallo-Roman heritage.
-
B.
Montluçon
Montluçon is a historic industrial town in central France known for its medieval old quarter and role as a key urban center in the Allier department.
-
C.
Cahors
Cahors is a historic town in southwestern France renowned for its medieval architecture, including the fortified Valentré Bridge, and its surrounding Malbec wine-producing vineyards.
-
D.
Mérignac
Mérignac is a suburban city in southwestern France, forming part of the Bordeaux metropolitan area and hosting the region’s main international airport.
-
E.
Angoulême
Angoulême is a historic city in southwestern France known for its hilltop old town, medieval ramparts, and status as a major center of the French comics 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_69d886d090cc8190a39cb94992586905 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3e80b7e6881909f7635875549a2f1 |
completed | April 18, 2026, 8:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a013a1062fc8190b1c4e97f42cf3faa |
completed | May 11, 2026, 2:08 a.m. |
Created at: April 10, 2026, 5:36 a.m.