Triple
T21119960
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MAC |
E520401
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object | MAC |
—
|
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: MAC | Statement: [MAC, shortName, MAC]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MAC Context triple: [MAC, shortName, MAC]
-
A.
MAC
The MAC is a regional museum in Spokane, Washington, featuring art, history, and cultural exhibits focused on the Inland Northwest.
-
B.
MAC
MAC is the common abbreviation for the Macon Bacon, a collegiate summer baseball team based in Macon, Georgia.
-
C.
MAC
MAC is a graduate-level professional degree focused on advanced accounting principles, financial reporting, and preparation for accounting certifications such as the CPA.
-
D.
MAC
MAC is the commonly used abbreviation for the United Kingdom’s Migration Advisory Committee, an independent body that advises the government on immigration policy.
-
E.
MAC
MAC is the stock ticker symbol for The Macerich Company, a real estate investment trust specializing in shopping centers and retail properties in the United States.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69e0b50a623881909c0bbaf4f2c055e7 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e72233a43481909535560514d388be |
completed | April 21, 2026, 7:07 a.m. |
Created at: April 16, 2026, 2:55 p.m.