Triple
T11942518
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MASP |
E284211
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | MASP |
E284211
|
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: MASP | Statement: [MASP, shortName, MASP]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MASP Context triple: [MASP, shortName, MASP]
-
A.
MASP
chosen
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.
-
B.
MAS
MAS is the ICAO airline designator used to identify Malaysia Airlines in international aviation operations.
-
C.
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.
-
D.
MAS
MAS is the official Indian Railways station code for Chennai Central, one of the busiest and most important railway terminals in South India.
-
E.
MAS
MAS is a sports club based in Saint-Étienne, France, known for its participation in regional football competitions.
- 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_69d6ab2db38c8190b1f0ed6663ef8ada |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d90342bb908190a019ac91a2b82f3d |
completed | April 10, 2026, 2:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f440a5a9c8819086a94ad60c6881b8 |
completed | May 1, 2026, 5:56 a.m. |
Created at: April 8, 2026, 9:45 p.m.