Triple
T36059913
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donnie Darko |
E1043046
|
entity |
| Predicate | inUniverseAuthorInfluence |
P134940
|
FINISHED |
| Object | Roberta Sparrow (Grandma Death) |
—
|
NE NERFINISHED |
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: Roberta Sparrow (Grandma Death) | Statement: [Donnie Darko, inUniverseAuthorInfluence, Roberta Sparrow (Grandma Death)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: inUniverseAuthorInfluence Context triple: [Donnie Darko, inUniverseAuthorInfluence, Roberta Sparrow (Grandma Death)]
-
A.
influencesFromAuthor
chosen
Indicates that one entity is affected or shaped by the actions, ideas, or characteristics of an author.
-
B.
inUniverseAuthorHome
Indicates that a location is the canonical home or primary residence of an author within a fictional universe.
-
C.
impactOnAuthor
Indicates that one entity has an effect, influence, or consequence on the author.
-
D.
workAuthoredInUniverse
Indicates that a creative work is set within, or narratively belongs to, a particular fictional or conceptual universe.
-
E.
hasInfluentialAuthor
Indicates that an entity has an author whose work has had significant impact or influence in a relevant domain.
- 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_69f76e2f09448190b0486d5ecad5e243 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fd2a215d6c8190a1a428ccaee603f1 |
completed | May 8, 2026, 12:11 a.m. |
| PD | Predicate disambiguation | batch_69fd28ef19688190bb8370f2812a43e7 |
completed | May 8, 2026, 12:06 a.m. |
Created at: May 3, 2026, 4:08 p.m.