Triple
T14397739
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | It Takes Two |
E356992
|
entity |
| Predicate | hasFictionalRelationshipType |
P34570
|
FINISHED |
| Object | caretaker-child relationship |
—
|
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: caretaker-child relationship | Statement: [It Takes Two, hasFictionalRelationshipType, caretaker-child relationship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalRelationshipType Context triple: [It Takes Two, hasFictionalRelationshipType, caretaker-child relationship]
-
A.
fictionalRelationship
chosen
Indicates a relationship that exists only within a fictional or imagined context between entities.
-
B.
hasProtagonistRelationship
Indicates that there exists a central, story-driving relationship involving the protagonist and another entity within a narrative.
-
C.
characterActorRelationship
Indicates a relationship where an actor portrays or is associated with a specific character in a work.
-
D.
showsRelationshipWith
Indicates that one entity visually or explicitly presents or demonstrates its connection or association with another entity.
-
E.
inRelationshipWith
Indicates that two entities are mutually involved in a defined personal, romantic, or partnership relationship with each other.
- 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_69d827927c988190ad98bb0360981783 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de90826f908190b3969af9b7cf922f |
completed | April 14, 2026, 7:07 p.m. |
| PD | Predicate disambiguation | batch_69de2aa024c48190805df6a9d63deb10 |
completed | April 14, 2026, 11:53 a.m. |
Created at: April 10, 2026, 1:17 a.m.