Triple
T26747581
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Betty Boop |
E674441
|
entity |
| Predicate | numberOfTheatricalShorts |
P196273
|
FINISHED |
| Object | over 90 |
—
|
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: over 90 | Statement: [Betty Boop, numberOfTheatricalShorts, over 90]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTheatricalShorts Context triple: [Betty Boop, numberOfTheatricalShorts, over 90]
-
A.
disneyPixarShortNumber
Indicates the ordinal position or identifying number assigned to a specific Disney-Pixar short film within a sequence or collection.
-
B.
hasCinematicShort
Indicates that an entity is associated with or includes a cinematic short film or short-form cinematic content.
-
C.
hasShortFilmLength
Indicates that an entity has a duration characteristic of a short film, typically below a standard feature-length runtime.
-
D.
notableShortFilm
Indicates that the subject is a short film that is recognized as notable or significant in some meaningful way.
-
E.
hasShortFilmAttachedInTheaters
Indicates that a film is accompanied by a specific short film when shown in theaters.
- 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_69eecda63a3881908095c47900692e65 |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69fe1fd637c08190aa95cd2478c278cb |
completed | May 8, 2026, 5:39 p.m. |
| PD | Predicate disambiguation | batch_69fe19344bb481909b5e2144155e4add |
completed | May 8, 2026, 5:11 p.m. |
| PDg | Predicate description generation | batch_69fe1fd58ad8819093d3d705e8521014 |
completed | May 8, 2026, 5:39 p.m. |
Created at: April 27, 2026, 3:52 a.m.