Triple
T13838781
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Girls Trip |
E332595
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object | Greg Gardiner |
E292629
|
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: Greg Gardiner | Statement: [Girls Trip, cinematographyBy, Greg Gardiner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Greg Gardiner Context triple: [Girls Trip, cinematographyBy, Greg Gardiner]
-
A.
Greg Gardiner
chosen
Greg Gardiner is a film cinematographer best known for his work on the popular Christmas comedy movie "Elf."
-
B.
Gerald Hagey
Gerald Hagey was a Canadian academic and administrator best known as the founding president who led the development of the University of Waterloo into a major institution.
-
C.
Gordon Thiessen
Gordon Thiessen is a Canadian economist who served as Governor of the Bank of Canada in the 1990s and early 2000s.
-
D.
Robert Chiarelli
Robert Chiarelli is a music producer and songwriter known for his work on tracks such as "The Apl Song."
-
E.
Gord Brown
Gord Brown was a Canadian Conservative politician who served as the long-time Member of Parliament for the Ontario riding of Leeds—Grenville.
- 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_69d81c5ae7c88190b0dd41bdafeb5999 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02ac6b7c81908d44632d6d628339 |
completed | April 14, 2026, 9:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c7062f548190a6a8d06ef2eefc9f |
completed | May 3, 2026, 10:07 p.m. |
Created at: April 9, 2026, 10:13 p.m.