Triple
T17917151
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monte Kay |
E447960
|
entity |
| Predicate | knownAs |
P39
|
FINISHED |
| Object | Monte Kay |
—
|
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: Monte Kay | Statement: [Monte Kay, knownAs, Monte Kay]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monte Kay Context triple: [Monte Kay, knownAs, Monte Kay]
-
A.
Monte Kay
chosen
Monte Kay was an American jazz producer and talent manager known for his influential work in the mid-20th-century music and entertainment industry.
-
B.
Monte Kiffin
Monte Kiffin is a veteran American football coach best known as a pioneering defensive coordinator in the NFL, particularly for developing the famed Tampa 2 defense with the Tampa Bay Buccaneers.
-
C.
Monte Brown
Monte Brown is a musician best known for his work with the new wave band Tom Tom Club.
-
D.
Monte Mor
Monte Mor is a municipality in the state of São Paulo, Brazil, known for its role in the Campinas metropolitan region and its growing industrial and residential development.
-
E.
Monte Blue
Monte Blue was an American film actor prominent during the silent era and early sound period, known for his leading and character roles in numerous Hollywood productions.
- 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_69d8b9f6d394819082a6d69fd1e23d2f |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4a30778fc81908b5b2e308fb158a5 |
completed | April 19, 2026, 9:40 a.m. |
Created at: April 10, 2026, 10:20 a.m.