Triple
T6975781
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cecil Gaines |
E161711
|
entity |
| Predicate | worksForInFiction |
P66101
|
FINISHED |
| Object | multiple U.S. presidents |
—
|
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: multiple U.S. presidents | Statement: [Cecil Gaines, worksForInFiction, multiple U.S. presidents]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: worksForInFiction Context triple: [Cecil Gaines, worksForInFiction, multiple U.S. presidents]
-
A.
worksForInNovel
chosen
Indicates that one entity is employed by or serves another entity within the fictional context of a specific novel.
-
B.
hasFictionalWork
Indicates that one entity is the creator, owner, or source of a fictional work associated with another entity.
-
C.
hasFictionComponent
Indicates that something includes, contains, or is composed in part of a fictional element or work.
-
D.
hasGroundsInFiction
Indicates that something is based on, justified by, or finds its origin within fictional works or narratives.
-
E.
fictionalOrigin
Indicates that one entity originates from, or was first introduced within, a fictional work, universe, or narrative created by another entity.
- 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_69c68854a0d88190bc0bf82263f1afce |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6db3d3ab08190b107f3229c357dd2 |
completed | March 27, 2026, 7:32 p.m. |
| PD | Predicate disambiguation | batch_69c6d7c262508190a7708b3d9cf23d7c |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:31 p.m.