Triple
T7809653
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Cat's-Paw |
E180644
|
entity |
| Predicate | hasHaroldLloydFeature |
P79139
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [The Cat's-Paw, hasHaroldLloydFeature, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHaroldLloydFeature Context triple: [The Cat's-Paw, hasHaroldLloydFeature, yes]
-
A.
hasGingerRogersRole
Indicates that an entity is assigned or associated with a role specifically identified as the "Ginger Rogers" role in a given context or production.
-
B.
hasChapmanCode
Indicates that an entity (typically a geographic or administrative area) is associated with a specific Chapman code used for standardized location identification.
-
C.
hasDirectorCameo
Indicates that the director of a work appears in a cameo role within that same work.
-
D.
hasInteractiveFilm
Indicates that an entity is associated with, offers, or features an interactive film experience.
-
E.
hasStuntDouble
Indicates that one entity serves as a stunt double who performs dangerous or physically demanding actions on behalf of another entity.
- F. None of above. chosen
Provenance (4 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_69ca827f6f148190beca4e245b993506 |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69caf78bb4b08190b2b3b51c5a0a033c |
completed | March 30, 2026, 10:22 p.m. |
| PD | Predicate disambiguation | batch_69cae91687788190af9cb7aaa996d291 |
completed | March 30, 2026, 9:20 p.m. |
| PDg | Predicate description generation | batch_69caf7855a3c81908b9318f7186fc0c0 |
completed | March 30, 2026, 10:21 p.m. |
Created at: March 30, 2026, 4:37 p.m.