Triple
T27354453
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TaleSpin |
E685645
|
entity |
| Predicate | usesAnthropomorphicCharacters |
P147364
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [TaleSpin, usesAnthropomorphicCharacters, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesAnthropomorphicCharacters Context triple: [TaleSpin, usesAnthropomorphicCharacters, true]
-
A.
hasAnthropomorphicAnimals
chosen
Indicates that something features animals with human-like characteristics, behaviors, or roles.
-
B.
isAnthropomorphic
Indicates that something non-human is given human-like characteristics, form, or behavior.
-
C.
hasFictionalAgriculturalCharacter
Indicates that an entity features or includes a character associated with agriculture within a fictional context.
-
D.
hasAnimatronicCharacter
Indicates that an entity features, includes, or is associated with an animatronic character.
-
E.
animatedCharacter
Indicates that an entity is a fictional character depicted through animation rather than live action.
- 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_69ef14887c288190931b8431fdbf53c4 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69f65f7731e4819099d5bd3d915ee266 |
completed | May 2, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69f65c1f94ac8190bc6fbc7916fc0d82 |
completed | May 2, 2026, 8:18 p.m. |
Created at: April 27, 2026, 11:50 a.m.