Triple
T36552620
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniel Isn’t Real |
E901309
|
entity |
| Predicate | hasImaginaryCompanionTheme |
P95848
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Daniel Isn’t Real, hasImaginaryCompanionTheme, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasImaginaryCompanionTheme Context triple: [Daniel Isn’t Real, hasImaginaryCompanionTheme, yes]
-
A.
hasImaginaryFriendCharacter
chosen
Indicates that an entity is associated with or has an imaginary friend character.
-
B.
hasFamilyTheme
Indicates that something involves, centers on, or prominently features themes related to family relationships or family life.
-
C.
hasFictionalPet
Indicates that an entity has, owns, or is associated with a pet that is fictional or imaginary.
-
D.
hasFictionalCompanion
Indicates that one entity has another entity as its fictional companion, typically within a narrative or imaginative context.
-
E.
hasDoppelgangerTheme
Indicates that something features or involves a doppelganger-related theme, such as doubles, look-alikes, or mirrored identities.
- 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_69f76e61217081908b79d610fe67b013 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fcc4b700748190ae00b21d09c96695 |
completed | May 7, 2026, 4:58 p.m. |
| PD | Predicate disambiguation | batch_69fcb0f9d3d881908a049475182fb039 |
completed | May 7, 2026, 3:34 p.m. |
Created at: May 3, 2026, 4:11 p.m.