Triple
T1283683
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vaucluse |
E27382
|
entity |
| Predicate | borders |
P224
|
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: [Vaucluse, borders, Var]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Var Context triple: [Vaucluse, borders, Var]
-
A.
Var
chosen
Var is a department in southeastern France known for its Mediterranean coastline, including popular resort areas along the French Riviera.
-
B.
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.
-
C.
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.
-
D.
Val
Val is the Allied reporting name for the Aichi D3A, a Japanese World War II carrier-based dive bomber used prominently in early Pacific naval battles.
-
E.
Vy
Vy is a major Norwegian railway and public transport company operating regional, intercity, and commuter train services across Norway and parts of Sweden.
- 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_69a496d3710c8190955dee8bc0dacb50 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c0b599ac819096fca9ada294d939 |
completed | March 1, 2026, 10:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aca2fdb3ac81909bc836e2a655130c |
completed | March 7, 2026, 10:13 p.m. |
Created at: March 1, 2026, 7:50 p.m.