Triple
T3882839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | boeuf bourguignon |
E92863
|
entity |
| Predicate | servedIn |
P253
|
FINISHED |
| Object | France |
E861
|
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: France | Statement: [boeuf bourguignon, servedIn, France]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: France Context triple: [boeuf bourguignon, servedIn, France]
-
A.
France
chosen
France is a major Western European nation known for its influential history, culture, and economy, and as a founding member of the European Union and the United Nations.
-
B.
France Ô
France Ô was a French public television channel dedicated to programming from France’s overseas departments and territories, operated by the France Télévisions group.
-
C.
France and Germany
France and Germany are two major neighboring European countries that share deep historical ties, a central role in the European Union, and a long land border.
-
D.
France 5
France 5 is a French public television channel known for its focus on educational, cultural, and documentary programming.
-
E.
France 4
France 4 is a French public television channel, part of the France Télévisions group, known for broadcasting youth-oriented and family entertainment programming.
- 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_69aed9697de0819087c2559295ff3d12 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeec8e8b3481909617ca0e37f8a6d4 |
completed | March 9, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5121874e8819088f79e1cf771a73a |
completed | March 14, 2026, 7:45 a.m. |
Created at: March 9, 2026, 3:20 p.m.