Triple
T14665875
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monte Blue |
E344371
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Monte Blue |
—
|
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 Blue | Statement: [Monte Blue, name, Monte Blue]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monte Blue Context triple: [Monte Blue, name, Monte Blue]
-
A.
Monte Blue
chosen
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.
-
B.
Monte Brown
Monte Brown is a musician best known for his work with the new wave band Tom Tom Club.
-
C.
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.
-
D.
Monte Frank
Monte Frank is a Connecticut attorney and civic leader who ran for governor as a third-party candidate in the 2018 Connecticut gubernatorial election.
-
E.
Monte Merrick
Monte Merrick is an American screenwriter best known for writing films such as the rodeo drama "8 Seconds" and the family comedy "Mr. Baseball."
- 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_69d822e283fc8190a0e4c235cf880052 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb54c69f8819080a37161deecfba8 |
completed | April 14, 2026, 9:44 p.m. |
Created at: April 10, 2026, 1:27 a.m.