Triple
T37110740
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thirty-Two Short Films About Glenn Gould |
E918982
|
entity |
| Predicate | portraysAspect |
P49090
|
FINISHED |
| Object | life of Glenn Gould |
—
|
LITERAL 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: life of Glenn Gould | Statement: [Thirty-Two Short Films About Glenn Gould, portraysAspect, life of Glenn Gould]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysAspect Context triple: [Thirty-Two Short Films About Glenn Gould, portraysAspect, life of Glenn Gould]
-
A.
portrayalFeature
chosen
Indicates that one entity serves as a characteristic, aspect, or attribute highlighted in the depiction or representation of another entity.
-
B.
portraysPersonAs
Indicates that one entity represents, depicts, or characterizes another person in a particular way or role.
-
C.
portraysActorAs
Indicates that one entity depicts or represents an actor in a particular role, character, or manner.
-
D.
portraysActivity
Indicates that one entity visually or narratively represents another entity engaged in a particular activity.
-
E.
portraysRoleTrait
Indicates that one entity depicts or represents a particular role or character trait of another entity.
- F. None of above.
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_69f76e9b99c8819096164b21ff5bd996 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69ff8aff48988190a48a440de8238ef9 |
completed | May 9, 2026, 7:29 p.m. |
| PD | Predicate disambiguation | batch_69ff8a780404819082f48ceb21e7fe11 |
completed | May 9, 2026, 7:26 p.m. |
Created at: May 3, 2026, 4:14 p.m.