Triple
T19408222
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lyon–Bron Airport |
E485518
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Bron |
—
|
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: Bron | Statement: [Lyon–Bron Airport, locatedIn, Bron]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bron Context triple: [Lyon–Bron Airport, locatedIn, Bron]
-
A.
Bron
chosen
Bron is a suburban commune in eastern France that forms part of the metropolitan area of Lyon.
-
B.
Bron
Bron is a British actress and writer known for her work in film, television, and radio since the 1960s.
-
C.
Brun
Brun is a given name and surname of Germanic origin, closely related to and often used as a variant of Bruno.
-
D.
Brandon
Brandon is a small city in eastern South Dakota that functions largely as a residential and commercial suburb of nearby Sioux Falls.
-
E.
Brandon
Brandon is a small historic town in Rutland County, Vermont, known for its classic New England charm and village center.
- 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_69d8e8d5162481909db12435d9535c1a |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e62af331108190b6b25ef8f386826e |
completed | April 20, 2026, 1:32 p.m. |
Created at: April 10, 2026, 1:36 p.m.