Triple
T11029163
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | By the Way, Meet Vera Stark |
E260712
|
entity |
| Predicate | hasFictionalFilmWithinPlay |
P97369
|
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: [By the Way, Meet Vera Stark, hasFictionalFilmWithinPlay, Yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalFilmWithinPlay Context triple: [By the Way, Meet Vera Stark, hasFictionalFilmWithinPlay, Yes]
-
A.
hasFictionalWork
Indicates that one entity is the creator, owner, or source of a fictional work associated with another entity.
-
B.
hasFictionalProductionType
Indicates that an entity is associated with a specific type or category of fictional production (such as a genre, format, or style).
-
C.
hasFictionalRole
Indicates that an entity plays or is assigned a specific role within a fictional work or narrative.
-
D.
producedFilm
Indicates that one entity served as the producer (or production company) responsible for making or financing the creation of a particular film.
-
E.
includedInFilm
Indicates that one entity (such as a scene, segment, or element) is contained within or forms part of a particular film.
- F. None of above. chosen
Provenance (4 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_69d6aa979bdc8190bf0e79104cc098c1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d797d2feb881909a5684721e8b0d9c |
completed | April 9, 2026, 12:13 p.m. |
| PD | Predicate disambiguation | batch_69d7440087ac8190aef2e6f6b13b2635 |
completed | April 9, 2026, 6:15 a.m. |
| PDg | Predicate description generation | batch_69d750c99f9881908ee2b01b6ce4b3a1 |
completed | April 9, 2026, 7:10 a.m. |
Created at: April 8, 2026, 9:25 p.m.