Triple
T18622442
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marmande station |
E455188
|
entity |
| Predicate | serves |
P98
|
FINISHED |
| Object | Marmande |
—
|
NE NERFINISHED |
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: Marmande | Statement: [Marmande station, serves, Marmande]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marmande Context triple: [Marmande station, serves, Marmande]
-
A.
Marmande
chosen
Marmande is a town in southwestern France known for its agricultural production, particularly tomatoes, and its location in the Garonne River valley.
-
B.
Saignon
Saignon is a picturesque hilltop village in southeastern France’s Vaucluse department, known for its medieval architecture and panoramic views over the Luberon valley.
-
C.
Saint-Mard
Saint-Mard is a French commune in the Seine-et-Marne department in the Île-de-France region, northeast of Paris.
-
D.
Orléat
Orléat is a small commune in central France’s Puy-de-Dôme department, known for its rural character within the Auvergne region.
-
E.
Morieux
Morieux is a coastal commune in the Côtes-d'Armor department of Brittany in northwestern France, known for its scenic shoreline along the English Channel.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d38cc7948190a55ea64e5638994e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e54f020fa08190bd78d72182496b19 |
completed | April 19, 2026, 9:54 p.m. |
Created at: April 10, 2026, 11:46 a.m.