Triple
T1461459
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Wishing-Chair |
E31519
|
entity |
| Predicate | includesCharacterType |
P5716
|
FINISHED |
| Object | pixies |
—
|
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: pixies | Statement: [The Wishing-Chair, includesCharacterType, pixies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesCharacterType Context triple: [The Wishing-Chair, includesCharacterType, pixies]
-
A.
hasTypicalCharacterType
Indicates that an entity is commonly associated with or exemplified by a particular type of character or persona.
-
B.
containsCharacter
chosen
Indicates that one entity includes a specific character as part of its content or composition.
-
C.
characterSetType
Indicates the type or category of character set associated with or used by an entity.
-
D.
basedOnCharacterBy
Indicates that one work, adaptation, or portrayal is derived from or inspired by a character created by another entity.
-
E.
legalCharacter
Indicates that an entity possesses a status, role, or nature that is recognized and defined by law.
- 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_69a49917dfc081909acdbdf5d684f1ef |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c59ecb60819082217b034e18381f |
completed | March 1, 2026, 11:02 p.m. |
| PD | Predicate disambiguation | batch_69a4c47ec5108190b1772237f2e5d90b |
completed | March 1, 2026, 10:58 p.m. |
Created at: March 1, 2026, 8 p.m.