Triple
T8788431
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TER Nouvelle-Aquitaine |
E209099
|
entity |
| Predicate | servesCity |
P82
|
FINISHED |
| Object | Niort |
E227001
|
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: Niort | Statement: [TER Nouvelle-Aquitaine, servesCity, Niort]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Niort Context triple: [TER Nouvelle-Aquitaine, servesCity, Niort]
-
A.
Niort
chosen
Niort is a historic city in western France known as an administrative and economic center, particularly for its strong mutual insurance and financial services sector.
-
B.
La Rochelle
La Rochelle is a historic French Atlantic port city that became a major stronghold and refuge for Huguenots during the French Wars of Religion.
-
C.
Nantes
Nantes is a historic port city in western France on the Loire River, known for its maritime heritage, cultural institutions, and vibrant arts scene.
-
D.
Luçon
Luçon is a historic town in western France, known as a former episcopal seat and for its notable cathedral and religious heritage.
-
E.
Saintes
Saintes is a historic town in southwestern France, known for its well-preserved Roman and medieval heritage, including ancient monuments and religious sites.
- 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_69ca836168108190bb43d3dc235c1f55 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5f89a84c819085d4cfe4e6dfbda8 |
completed | March 31, 2026, 11:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d054339b988190849092f178a7af2c |
completed | April 3, 2026, 11:58 p.m. |
Created at: March 30, 2026, 6:43 p.m.