Triple
T23265014
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | XM |
E588121
|
entity |
| Predicate | assignedTo |
P3151
|
FINISHED |
| Object | J-Air |
—
|
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: J-Air | Statement: [XM, assignedTo, J-Air]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: J-Air Context triple: [XM, assignedTo, J-Air]
-
A.
J-Air
chosen
J-Air is a Japanese regional airline operating domestic feeder and short-haul routes on behalf of Japan Airlines.
-
B.
L’Air
L’Air is a celebrated early 20th-century sculpture by Aristide Maillol that exemplifies his serene, classical approach to the female form.
-
C.
Canair
Canair was a Spanish regional airline that operated inter-island flights in the Canary Islands.
-
D.
Consair
Consair is an abbreviated name historically used for Consolidated Aircraft, a major American aerospace manufacturer known for producing military aircraft such as the B-24 Liberator during World War II.
-
E.
Aerograd
Aerograd is a 1935 Soviet science fiction and propaganda film directed by Alexander Dovzhenko, set in a futuristic Far Eastern border town threatened by foreign and internal enemies.
- 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_69e25d148adc819088efbf42672604e9 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f194cc3b908190aaefd036aa2b52b5 |
completed | April 29, 2026, 5:19 a.m. |
Created at: April 17, 2026, 4:29 p.m.