Triple
T8129582
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peppermint |
E189820
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object | David Lanzenberg |
E366661
|
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: David Lanzenberg | Statement: [Peppermint, cinematographyBy, David Lanzenberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: David Lanzenberg Context triple: [Peppermint, cinematographyBy, David Lanzenberg]
-
A.
David Lanzenberg
chosen
David Lanzenberg is a film cinematographer known for his work on feature films such as "Paper Towns."
-
B.
Michael Lehmann
Michael Lehmann is an American film and television director best known for the dark comedy "Heathers" and various other Hollywood comedies.
-
C.
David Weinberg
David Weinberg is a name shared by multiple notable individuals, including professionals in fields such as science, academia, and the arts.
-
D.
Eric Tannenbaum
Eric Tannenbaum is a television producer best known for his work on popular American sitcoms, including serving as an executive producer on "Two and a Half Men."
-
E.
Chris Lebenzon
Chris Lebenzon is an American film editor known for his long-time collaborations with directors like Tim Burton and Tony Scott on major Hollywood films.
- 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_69ca82bcb4848190a9a9d036ad768642 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb43b6b4dc8190be237e6dd21c863b |
completed | March 31, 2026, 3:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cde709d3188190b3a954a613c3c5b2 |
completed | April 2, 2026, 3:48 a.m. |
Created at: March 30, 2026, 5:34 p.m.