Triple
T5120810
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aéroports de la Côte d’Azur |
E115457
|
entity |
| Predicate | servesRegion |
P82
|
FINISHED |
| Object | Var |
E84932
|
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: Var | Statement: [Aéroports de la Côte d’Azur, servesRegion, Var]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Var Context triple: [Aéroports de la Côte d’Azur, servesRegion, Var]
-
A.
Var
Var is a Norse goddess associated with oaths, agreements, and the punishment of those who break them.
-
B.
Var
chosen
Var is a department in southeastern France known for its Mediterranean coastline, including popular resort areas along the French Riviera.
-
C.
Vars
Vars is a French alpine commune and ski resort village located in the Hautes-Alpes department in southeastern France.
-
D.
VAR
VAR (Video Assistant Referee) is a football officiating system that uses video technology to help referees review and correct clear and obvious errors in key match situations.
-
E.
Varig
Varig was Brazil’s former flagship airline, once the country’s largest carrier and a major international operator throughout much of the 20th century.
- 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_69bd4442ade0819087b9461f892b206b |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd78015ad88190a3e51da494c19e30 |
completed | March 20, 2026, 4:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bec4b0578c819081ad7554bafafe49 |
completed | March 21, 2026, 4:17 p.m. |
Created at: March 20, 2026, 1:42 p.m.