Triple
T22100713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kind Hearts and Coronets |
E546164
|
entity |
| Predicate | numberOfRolesPlayedByAlecGuinness |
P32517
|
FINISHED |
| Object | 9 |
—
|
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: 9 | Statement: [Kind Hearts and Coronets, numberOfRolesPlayedByAlecGuinness, 9]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfRolesPlayedByAlecGuinness Context triple: [Kind Hearts and Coronets, numberOfRolesPlayedByAlecGuinness, 9]
-
A.
hasPortrayedPersonRole
Indicates that an entity has performed or held a specific role in portraying a particular person (e.g., in a film, play, or other representation).
-
B.
featuresActorInMultipleRoles
chosen
Indicates that a work includes an actor who portrays more than one distinct role within that same work.
-
C.
hasPortrayedRole
Indicates that an entity has performed or depicted a specific role or character, typically in a work such as a film, play, or television show.
-
D.
numberOfActors
Indicates the total count of actors associated with a given entity or context.
-
E.
oftenPlayedBy
Indicates that one entity frequently performs, portrays, or executes another entity, such as a role, character, or piece of music.
- 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_69e11e378dc08190896d6a51597afd5a |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f1291501508190ad5689be5abb2ba6 |
completed | April 28, 2026, 9:39 p.m. |
| PD | Predicate disambiguation | batch_69e71b20ec50819096ac196c798f8e3c |
completed | April 21, 2026, 6:37 a.m. |
Created at: April 16, 2026, 8:30 p.m.