Triple
T2708964
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dordogne River |
E59810
|
entity |
| Predicate | flowsThroughTown |
P42402
|
FINISHED |
| Object | Bergerac |
E206236
|
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: Bergerac | Statement: [Dordogne River, flowsThroughTown, Bergerac]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bergerac Context triple: [Dordogne River, flowsThroughTown, Bergerac]
-
A.
Bergerac
chosen
Bergerac is a notable wine-producing area in southwestern France, recognized for its diverse red, white, and dessert wines.
-
B.
Château Rouge
Château Rouge is a Paris Métro station in the 18th arrondissement, serving the multicultural Château Rouge neighborhood near Montmartre.
-
C.
Le Vigan
Le Vigan is a historic market town in southern France that serves as one of the main gateways to the Cévennes mountain region.
-
D.
Margeride
Margeride is a mountainous and sparsely populated region in south-central France known for its granite plateaus, forests, and traditional rural landscapes.
-
E.
Lebrun
Lebrun is a French surname borne by various notable figures in politics, arts, and other fields.
- 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_69ab4ac92a088190bc74bca14038e3de |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abdd1fc30c81909ac06588d50abdf8 |
completed | March 7, 2026, 8:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afaf7f99508190acfd00baec64b7e9 |
completed | March 10, 2026, 5:43 a.m. |
Created at: March 6, 2026, 9:55 p.m.