Triple
T6894577
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | XiamenAir |
E159138
|
entity |
| Predicate | IATAAirlineCode |
P12360
|
FINISHED |
| Object | MF |
E159137
|
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: MF | Statement: [XiamenAir, IATAAirlineCode, MF]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MF Context triple: [XiamenAir, IATAAirlineCode, MF]
-
A.
MF
chosen
MF is the two-letter IATA airline designator assigned to XiamenAir, a major Chinese carrier based in Xiamen.
-
B.
MF
MF is the two-letter ISO 3166-1 alpha-2 country code assigned to the French overseas collectivity of Saint Martin.
-
C.
MJ
MJ is the widely used nickname for Michael Jordan, the legendary American basketball player often regarded as the greatest in NBA history.
-
D.
MJ
MJ is a reimagined version of the Mary Jane Watson character who appears as Peter Parker’s sharp, observant classmate and love interest in the Marvel Cinematic Universe Spider-Man films.
-
E.
MJ
MJ is a Master of Jurisprudence graduate law degree designed for non-lawyers seeking advanced legal knowledge in a specific field.
- 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_69c6883568c8819081db6407e892cccc |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d931da24819096b9b205f2c0ebb0 |
completed | March 27, 2026, 7:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c748db2bd48190bb26f60c58ec8229 |
completed | March 28, 2026, 3:19 a.m. |
Created at: March 27, 2026, 2:24 p.m.