Triple
T14646501
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arles railway station |
E343863
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object | Nîmes station |
E596244
|
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: Nîmes station | Statement: [Arles railway station, adjacentTo, Nîmes station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nîmes station Context triple: [Arles railway station, adjacentTo, Nîmes station]
-
A.
Nîmes station
chosen
Nîmes station is a major railway hub in southern France that connects the city of Nîmes to regional and high-speed national rail networks.
-
B.
Béziers station
Béziers station is the main railway station serving the city of Béziers in southern France, providing regional and long-distance train connections.
-
C.
Agde station
Agde station is a railway station in the town of Agde in southern France, serving regional and long-distance passenger trains.
-
D.
Aix-en-Provence TGV station
Aix-en-Provence TGV station is a high-speed railway station in southern France serving the city of Aix-en-Provence with TGV connections on the national high-speed network.
-
E.
Alésia station
Alésia station is a Paris Métro station on Line 4 located in the 14th arrondissement of Paris.
- 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_69d822e1a2cc81908e5bb93cf61ce3cc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb4ebe8048190a2935d00c9cfd8be |
completed | April 14, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdf07acd748190b2821dee21ecd740 |
completed | May 8, 2026, 2:17 p.m. |
Created at: April 10, 2026, 1:26 a.m.