Triple
T8720063
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TER Normandie |
E206987
|
entity |
| Predicate | connectsCity |
P4245
|
FINISHED |
| Object | Fécamp |
E366745
|
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: Fécamp | Statement: [TER Normandie, connectsCity, Fécamp]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fécamp Context triple: [TER Normandie, connectsCity, Fécamp]
-
A.
Fécamp
chosen
Fécamp is a coastal town and former important fishing port in northern France, known for its historic Benedictine Palace and dramatic cliffs along the English Channel.
-
B.
Colleville-sur-Mer
Colleville-sur-Mer is a commune in Normandy, France, best known for its location by the D-Day landing beaches and the Normandy American Cemetery.
-
C.
Isigny-sur-Mer
Isigny-sur-Mer is a coastal commune in Normandy, France, renowned for its dairy products—especially butter and cream—and its historic port.
-
D.
Boulogne-sur-Mer
Boulogne-sur-Mer is a coastal city and major fishing port in northern France, located on the English Channel in the Pas-de-Calais department.
-
E.
Avranches
Avranches is a historic town in northwestern France, near Mont-Saint-Michel, known for its medieval heritage and role in the liberation of Normandy during World War II.
- 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_69ca835811d8819081ea00fd2a2c9a1c |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5d02a52c81909f93622ae6920b80 |
completed | March 31, 2026, 11:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf88cf76888190a0cdab7f30c791c7 |
completed | April 3, 2026, 9:30 a.m. |
Created at: March 30, 2026, 6:36 p.m.