Triple
T5044507
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mons |
E113627
|
entity |
| Predicate | twinTown |
P1072
|
FINISHED |
| Object | Vannes |
E162998
|
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: Vannes | Statement: [Mons, twinTown, Vannes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vannes Context triple: [Mons, twinTown, Vannes]
-
A.
Vannes
chosen
Vannes is a historic coastal city in northwestern France known for its well-preserved medieval old town and harbor on the Gulf of Morbihan.
-
B.
Quimper
Quimper is a historic city in western France known for its medieval old town, Gothic cathedral, and traditional Breton culture.
-
C.
Rennes
Rennes is the capital city of France’s Brittany region, known for its historic medieval center, vibrant student population, and role as a major cultural and economic hub in western France.
-
D.
Concarneau
Concarneau is a coastal town and fishing port in Brittany, France, known for its walled medieval "Ville Close" and maritime heritage.
-
E.
Luçon
Luçon is a historic town in western France, known as a former episcopal seat and for its notable cathedral and religious heritage.
- 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_69bd44391fc48190a311ce9c826c209b |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd73fd81788190b7799f519277119a |
completed | March 20, 2026, 4:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be9c8ad6408190bf4408af7b095a12 |
completed | March 21, 2026, 1:26 p.m. |
Created at: March 20, 2026, 1:37 p.m.