Triple
T20814208
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mont Gros |
E512391
|
entity |
| Predicate | hasNameInLanguage |
P15
|
FINISHED |
| Object | Mont Gros@fr |
—
|
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: Mont Gros@fr | Statement: [Mont Gros, hasNameInLanguage, Mont Gros@fr]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mont Gros@fr Context triple: [Mont Gros, hasNameInLanguage, Mont Gros@fr]
-
A.
Mont Gros
chosen
Mont Gros is a hill overlooking Nice on the French Riviera, known for hosting the historic Observatoire de Nice astronomical observatory.
-
B.
Gros
Gros is a French surname most famously borne by Antoine-Jean Gros, a prominent Napoleonic-era painter known for his dramatic historical and battle scenes.
-
C.
Gros
Gros is a lively neighborhood in Donostia-San Sebastián, Spain, known for its surf-friendly Zurriola Beach, youthful atmosphere, and vibrant bars and restaurants.
-
D.
LeGros
LeGros is a surname most notably associated with American actor James LeGros, known for his work in independent films and television.
-
E.
Gros Plant
Gros Plant is a light, high-acid white grape variety from France’s Loire Valley, traditionally used to produce crisp, refreshing wines often enjoyed with seafood.
- 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_69e0b4cd25088190b48ca9700cd24efc |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2d4e43c8190aecce82a3f7e2de0 |
completed | April 21, 2026, 12:20 a.m. |
Created at: April 16, 2026, 12:41 p.m.