Triple
T13909548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roland-Michel Barrin de La Galissonière |
E334449
|
entity |
| Predicate | birthPlace |
P1
|
FINISHED |
| Object | Rochefort |
E365038
|
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: Rochefort | Statement: [Roland-Michel Barrin de La Galissonière, birthPlace, Rochefort]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rochefort Context triple: [Roland-Michel Barrin de La Galissonière, birthPlace, Rochefort]
-
A.
Rochefort
chosen
Rochefort is a historic French port town on the Atlantic coast known for its naval heritage and maritime museum sites.
-
B.
Rochefort
Rochefort is a town in the Walloon region of Belgium, known for its historic abbey and Trappist beer.
-
C.
Rochefort
Rochefort is a municipality in the canton of Neuchâtel in western Switzerland.
-
D.
Niort
Niort is a historic city in western France known as an administrative and economic center, particularly for its strong mutual insurance and financial services sector.
-
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_69d81c5eaa9c819083b1ff8689179565 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2721ec6c8190888f4a9d004eb8e0 |
completed | April 14, 2026, 11:38 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c726b7388190b557f4c41622460d |
completed | May 3, 2026, 10:07 p.m. |
Created at: April 9, 2026, 10:16 p.m.