Triple
T13994061
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amy (2015 documentary film) |
E336649
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object | Matt Curtis |
E526676
|
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: Matt Curtis | Statement: [Amy (2015 documentary film), cinematographyBy, Matt Curtis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Matt Curtis Context triple: [Amy (2015 documentary film), cinematographyBy, Matt Curtis]
-
A.
Matt Curtis
chosen
Matt Curtis is a cinematographer known for his work on the film "Amy."
-
B.
Greg Curtis
Greg Curtis is a songwriter best known for co-writing Christina Aguilera’s pop single "Not Myself Tonight."
-
C.
Curtis Heath
Curtis Heath is a film composer and musician known for creating the score for the indie comedy-drama "Support the Girls."
-
D.
Matt Burke
Matt Burke is a retired Australian rugby union player renowned for his long and successful career with the Wallabies and the New South Wales Waratahs, primarily as a fullback and goal-kicker.
-
E.
Matt Burke
Matt Burke is a retired English teacher and key supporting character in Stephen King’s novel "’Salem’s Lot," who helps protagonist Ben Mears confront the town’s growing vampire threat.
- 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_69d81c639e808190a0e4b4f3d31c6a59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2eb53f508190855cd69b8061dd77 |
completed | April 14, 2026, 12:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feadf7fee48190bf58a1b4a603217e |
completed | May 9, 2026, 3:46 a.m. |
Created at: April 9, 2026, 10:19 p.m.