Triple
T6318654
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Métropole Nice Côte d’Azur |
E141678
|
entity |
| Predicate | includesMunicipality |
P14658
|
FINISHED |
| Object | Lantosque |
E362740
|
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: Lantosque | Statement: [Métropole Nice Côte d’Azur, includesMunicipality, Lantosque]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lantosque Context triple: [Métropole Nice Côte d’Azur, includesMunicipality, Lantosque]
-
A.
Lantosque
chosen
Lantosque is a small picturesque commune in southeastern France, nestled in the Vésubie Valley of the Alpes-Maritimes department in the Provence-Alpes-Côte d'Azur region.
-
B.
Balzar
Balzar is a town and agricultural center in coastal Ecuador, known for its rice and banana production within Guayas Province.
-
C.
Levasy
Levasy is a small city located in Jackson County in the U.S. state of Missouri.
-
D.
Valleiry
Valleiry is a small French commune in the Haute-Savoie department of the Auvergne-Rhône-Alpes region in southeastern France, near the Swiss border.
-
E.
Saffais
Saffais is a small commune in the Meurthe-et-Moselle department of northeastern France.
- 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_69c008d13b8c8190be47d896eb735605 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c064c38fe48190a71a4e5e1af19b10 |
completed | March 22, 2026, 9:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c5e47ecea08190828af72d30d69a8c |
completed | March 27, 2026, 1:59 a.m. |
Created at: March 22, 2026, 4:29 p.m.