Triple
T27104723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Big Business |
E686534
|
entity |
| Predicate | filmStarDuo |
P129510
|
FINISHED |
| Object | Laurel and Hardy |
—
|
NE NERFINISHED |
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: Laurel and Hardy | Statement: [Big Business, filmStarDuo, Laurel and Hardy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmStarDuo Context triple: [Big Business, filmStarDuo, Laurel and Hardy]
-
A.
filmCoStar
chosen
Indicates that two people appeared together as co-actors in the same film.
-
B.
performedInFilmOpposite
Indicates that two performers acted together in significant, often directly interacting roles in the same film.
-
C.
starredActor
Indicates that an actor performed a leading or significant role in a particular production or work.
-
D.
directorSpouseInCast
Indicates that a film’s director is married to someone who appears as a cast member in that same film.
-
E.
hasTwinActors
Indicates that two or more actors share a twin relationship, typically portraying twin characters or being treated as twins within a given context.
- 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_69ef148accd48190b6ed6e13a15f2a4f |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69f623fcc6c881908e76b65c0ee51dd4 |
completed | May 2, 2026, 4:19 p.m. |
| PD | Predicate disambiguation | batch_69f620e0b37481909a280574decbd443 |
completed | May 2, 2026, 4:05 p.m. |
Created at: April 27, 2026, 8:50 a.m.