Triple
T20657978
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taxi 2 |
E507676
|
entity |
| Predicate | filmingLocation |
P40
|
FINISHED |
| Object | Marseille |
—
|
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: Marseille | Statement: [Taxi 2, filmingLocation, Marseille]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marseille Context triple: [Taxi 2, filmingLocation, Marseille]
-
A.
Marseille
chosen
Marseille is a historic Mediterranean port city in southern France known for its diverse culture, maritime heritage, and role as a major economic hub.
-
B.
Marseillan
Marseillan is a coastal commune in southern France known for its historic port, oyster farming, and proximity to the Étang de Thau lagoon.
-
C.
Lyon
Lyon is a historic Scottish noble family name most prominently associated with the Earls of Strathmore and Kinghorne.
-
D.
Lyon
Lyon is a major city in east-central France known for its historical and architectural landmarks, gastronomy, and role as a key economic and cultural center.
-
E.
Toulon
Toulon is a major port city on France’s Mediterranean coast that serves as the principal base of the French Navy.
- 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_69e0b4bf58c081908e52a4500e03ff83 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6b2eefd5c8190a71d4be690a6ae0e |
completed | April 20, 2026, 11:12 p.m. |
Created at: April 16, 2026, 11:43 a.m.