Triple
T4042455
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kristen |
E83981
|
entity |
| Predicate | hasOnScreenRelationships |
P46690
|
FINISHED |
| Object | multiple male characters in Think Like a Man |
—
|
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: multiple male characters in Think Like a Man | Statement: [Kristen, hasOnScreenRelationships, multiple male characters in Think Like a Man]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOnScreenRelationships Context triple: [Kristen, hasOnScreenRelationships, multiple male characters in Think Like a Man]
-
A.
onScreenSibling
Indicates that two characters are depicted as siblings within an on-screen or fictional context.
-
B.
hasScreen
Indicates that an entity is equipped with or includes a screen or display component.
-
C.
hasOnscreenPartner
chosen
Indicates that one entity appears together with another as a partner within the same onscreen context or scene.
-
D.
hasRelation
Indicates that there exists some specified relationship or association between two entities.
-
E.
hasKeyRelationship
Indicates a relationship where one entity serves as a key (e.g., identifier, access token, or primary reference) that grants access to, controls, or uniquely identifies another entity.
- 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_69aed92f7cf0819098e0539bdcc3767f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefb5b65c08190ba3f340ed18737f8 |
completed | March 9, 2026, 4:54 p.m. |
| PD | Predicate disambiguation | batch_69aef900386481909d04555a9ec9b0e3 |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:37 p.m.