Triple
T29321856
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stewjon |
E743535
|
entity |
| Predicate | hasFictionalMedium |
P194001
|
FINISHED |
| Object | film tie-in material |
—
|
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: film tie-in material | Statement: [Stewjon, hasFictionalMedium, film tie-in material]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalMedium Context triple: [Stewjon, hasFictionalMedium, film tie-in material]
-
A.
hasFictionalUniverseMedium
chosen
Indicates that a fictional universe is presented or expressed through a particular medium (such as a book, film, game, etc.).
-
B.
hasFictionalType
Indicates that an entity is associated with or classified under a particular type or category that is fictional rather than real.
-
C.
hasFictionalProductionType
Indicates that an entity is associated with a specific type or category of fictional production (such as a genre, format, or style).
-
D.
hasFictionalContent
Indicates that something contains or includes material that is imaginary, invented, or not intended to represent real events or facts.
-
E.
hasFictionalDepictions
Indicates that an entity is represented or portrayed in one or more fictional works or narratives.
- 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_69f09125f784819080f4e9fce9fe624f |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69fd91a5dad8819093eeeef527027890 |
completed | May 8, 2026, 7:32 a.m. |
| PD | Predicate disambiguation | batch_69fd8f65fe9081908902500a3228d935 |
completed | May 8, 2026, 7:23 a.m. |
Created at: April 28, 2026, 1:23 p.m.