Triple
T18790629
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lille Airport |
E459503
|
entity |
| Predicate | hasIATACode |
P2569
|
FINISHED |
| Object | LIL |
—
|
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: LIL | Statement: [Lille Airport, hasIATACode, LIL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: LIL Context triple: [Lille Airport, hasIATACode, LIL]
-
A.
LIL
chosen
LIL is the IATA airport code for Lille Airport, the main international airport serving the Lille metropolitan area in northern France.
-
B.
Lil
Lil is the nickname and given name of Lil Hardin Armstrong, a pioneering American jazz pianist, composer, and bandleader.
-
C.
Lil
Lil is a key character in Cory Doctorow's science fiction novel "Down and Out in the Magic Kingdom," set in a reputation-based future society centered around a Disney World-like theme park.
-
D.
Lil
Lil is a character portrayed by Maria Bello, best known as the tough, enigmatic bar owner in the film "Coyote Ugly."
-
E.
Lil B
Lil B is an American rapper and internet cult figure known for his prolific output, unconventional style, and the creation of the "Based" philosophy and persona.
- 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_69d8d396f54c8190ba49db31e8743842 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5978599008190aaceaff1b1e0a2c7 |
completed | April 20, 2026, 3:03 a.m. |
Created at: April 10, 2026, 11:53 a.m.