Triple
T17844739
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Villeneuve-Loubet |
E445628
|
entity |
| Predicate | locatedOnRiver |
P165
|
FINISHED |
| Object | Loup |
—
|
NE NERFINISHED |
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: Loup | Statement: [Villeneuve-Loubet, locatedOnRiver, Loup]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Loup Context triple: [Villeneuve-Loubet, locatedOnRiver, Loup]
-
A.
Loup
chosen
The Loup is a river in southeastern France that flows through the Alpes-Maritimes department, known for its scenic gorges and popular outdoor recreation areas.
-
B.
Lobo
Lobo is a surname most prominently associated with Rebecca Lobo, a former American professional basketball player and Hall of Famer.
-
C.
Lobo
Lobo is an American soft rock singer-songwriter best known for his 1970s hits such as "Me and You and a Dog Named Boo" and "I'd Love You to Want Me."
-
D.
Lobo
Lobo is a coastal municipality in the province of Batangas in the Philippines, known for its beaches, dive sites, and marine biodiversity.
-
E.
Lobo
Lobo is a violent, wisecracking intergalactic bounty hunter and antihero from DC Comics known for his immense strength, regenerative abilities, and over-the-top brutality.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9f1a6d881909f024bc603111cdb |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e48ff980048190b496c55b83b3b318 |
completed | April 19, 2026, 8:19 a.m. |
Created at: April 10, 2026, 10:16 a.m.