Triple
T2840678
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tarn River |
E62457
|
entity |
| Predicate | crosses |
P416
|
FINISHED |
| Object | Montauban |
E112838
|
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: Montauban | Statement: [Tarn River, crosses, Montauban]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Montauban Context triple: [Tarn River, crosses, Montauban]
-
A.
Montauban
chosen
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.
-
B.
Gradignan
Gradignan is a suburban commune in southwestern France’s Gironde department, forming part of the Bordeaux metropolitan area and known for its green spaces and wine-growing surroundings.
-
C.
La Grande-Motte
La Grande-Motte is a seaside resort town on France’s Mediterranean coast, noted for its distinctive modernist pyramid-shaped architecture and beaches.
-
D.
Béziers
Béziers is a historic city in southern France known for its wine production, ancient Roman heritage, and the famous Feria de Béziers festival.
-
E.
Yssingeaux
Yssingeaux is a commune in south-central France that serves as an administrative and service center in the Haute-Loire department.
- 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_69ab4c3d16bc81908b3a1c98fbd287fe |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdf15b7288190a03d1193cc0544a6 |
completed | March 7, 2026, 8:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b055d9c0f08190b62afbc1bf98dc20 |
completed | March 10, 2026, 5:33 p.m. |
Created at: March 6, 2026, 10:01 p.m.