Triple
T3017141
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MALAYSIAN |
E82363
|
entity |
| Predicate | associatedWithICAO |
P26821
|
FINISHED |
| Object | MAS |
E82362
|
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: MAS | Statement: [MALAYSIAN, associatedWithICAO, MAS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MAS Context triple: [MALAYSIAN, associatedWithICAO, MAS]
-
A.
MAS
chosen
MAS is the ICAO airline designator used to identify Malaysia Airlines in international aviation operations.
-
B.
MAS
MAS (Monetary Authority of Singapore) is Singapore’s central bank and integrated financial regulator, responsible for monetary policy, financial supervision, and the stability of the country’s financial system.
-
C.
MASP
MASP (Museu de Arte de São Paulo) is one of Brazil’s most important art museums, renowned for its striking modernist architecture and extensive collection of Western and Brazilian art.
-
D.
MSA
MSA is the standardized, literary form of Arabic used in formal writing, media, education, and official communication across the Arab world.
-
E.
MSA
MSA is a common abbreviation for a metropolitan statistical area, a region defined by the U.S. Office of Management and Budget for statistical and demographic analysis.
- 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_69ad8b1eb53481908c39bbcd1ec104b2 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9a6c56708190b7d8d08bca727cc1 |
completed | March 8, 2026, 3:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b12e6eac1481909d56844e53c37b59 |
completed | March 11, 2026, 8:57 a.m. |
Created at: March 8, 2026, 3 p.m.