Triple
T5693325
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chris Messina |
E125476
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Air |
E39490
|
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: Air | Statement: [Chris Messina, notableWork, Air]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Air Context triple: [Chris Messina, notableWork, Air]
-
A.
Air
chosen
"Air" is a 2023 biographical sports drama film directed by Ben Affleck that chronicles Nike's pursuit of a young Michael Jordan, in which Matt Damon stars as talent scout Sonny Vaccaro.
-
B.
Air
Air is a French electronic music duo known for their atmospheric, downtempo sound and influential albums like "Moon Safari."
-
C.
Aero
Aero is a high-performance, sport-oriented trim level used by Saab for its 9-3 and other models, typically featuring more powerful engines and upgraded equipment.
-
D.
Luft
Luft is a surname most notably associated with Sid Luft, the American film producer and third husband of entertainer Judy Garland.
-
E.
9 Air
9 Air is a Chinese low-cost airline based in Guangzhou that operates domestic and regional passenger flights.
- 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_69c0082bb19c8190823a4facd3cba79b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c023e7dbe48190850b501f223614e3 |
completed | March 22, 2026, 5:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c05a4f2bfc8190bc56c094f9ae9ce1 |
completed | March 22, 2026, 9:08 p.m. |
Created at: March 22, 2026, 3:44 p.m.