Triple
T13679236
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bordeaux–Sète railway |
E327954
|
entity |
| Predicate | serves |
P98
|
FINISHED |
| Object | Sète |
E157839
|
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: Sète | Statement: [Bordeaux–Sète railway, serves, Sète]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sète Context triple: [Bordeaux–Sète railway, serves, Sète]
-
A.
Sète
chosen
Sète is a coastal port city in southern France known for its canals, fishing industry, and vibrant maritime culture on the Mediterranean Sea.
-
B.
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.
-
C.
Perpignan
Perpignan is a historic city in southern France near the Spanish border, known for its Catalan culture and Mediterranean climate.
-
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.
Villefranche-sur-Mer
Villefranche-sur-Mer is a picturesque coastal town in southeastern France known for its deep natural harbor, colorful old town, and scenic setting on the Mediterranean Sea.
- 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_69d8076f1fa8819094664a59b55010df |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc66cbb088190907cb89dda8e4ebd |
completed | April 12, 2026, 4:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fbac7c8bbc81908c7aa15c6892be34 |
completed | May 6, 2026, 9:02 p.m. |
Created at: April 9, 2026, 9:53 p.m.