Triple
T20158834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | VAL 206 |
E491648
|
entity |
| Predicate | usedOn |
P2367
|
FINISHED |
| Object | Rennes Metro |
—
|
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: Rennes Metro | Statement: [VAL 206, usedOn, Rennes Metro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rennes Metro Context triple: [VAL 206, usedOn, Rennes Metro]
-
A.
Rennes Metro
chosen
Rennes Metro is the rapid transit system serving the city of Rennes in France, providing urban rail transport across the metropolitan area.
-
B.
Rennes metro station
Rennes metro station is a Paris Métro station on Line 12 located in the city's 6th arrondissement, serving the Saint-Germain-des-Prés area on the Left Bank.
-
C.
Nantes tramway
The Nantes tramway is a modern light rail network in Nantes, France, that serves as a key component of the city's public transportation system.
-
D.
Lille Metro
The Lille Metro is a fully automated light metro system serving the city of Lille and its metropolitan area in northern France.
-
E.
Lyon Metro
Lyon Metro is the rapid transit system serving the French city of Lyon and its suburbs, known for its rubber-tyred lines and integration with the city’s broader public transport network.
- 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_69da6266c6888190bc1a3ecf24814d34 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e667e27aa88190a326288b992ea274 |
completed | April 20, 2026, 5:52 p.m. |
Created at: April 11, 2026, 11:34 p.m.