Triple
T37595546
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The 14 Fists of McCluskey |
E935373
|
entity |
| Predicate | portraysRickDaltonAs |
P100368
|
FINISHED |
| Object | tough-guy movie star |
—
|
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: tough-guy movie star | Statement: [The 14 Fists of McCluskey, portraysRickDaltonAs, tough-guy movie star]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysRickDaltonAs Context triple: [The 14 Fists of McCluskey, portraysRickDaltonAs, tough-guy movie star]
-
A.
portraysActorAs
Indicates that one entity depicts or represents an actor in a particular role, character, or manner.
-
B.
roleInTheGrandBudapestHotel
Indicates that an entity has a specific role or part in the context of "The Grand Budapest Hotel" (such as a character, performer, or production role).
-
C.
portraysPersonAs
chosen
Indicates that one entity represents, depicts, or characterizes another person in a particular way or role.
-
D.
portrayedArtist
Indicates that one entity has depicted or represented another entity as an artist in some medium or work.
-
E.
portraysCharacterIn
Indicates that one entity depicts or represents a particular character within a work, such as a film, show, or other narrative medium.
- 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_69f76ecf39c081909baffe597bb55273 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69ffaa7bc45c8190b907db8579244a7b |
completed | May 9, 2026, 9:43 p.m. |
| PD | Predicate disambiguation | batch_69ffa9f6c9a481908fbd4d18b311cbe2 |
completed | May 9, 2026, 9:41 p.m. |
Created at: May 3, 2026, 4:18 p.m.