Triple
T33422524
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Outstanding Supporting Actor in a Drama Series |
E855881
|
entity |
| Predicate | isForFictionalWork |
P176587
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Outstanding Supporting Actor in a Drama Series, isForFictionalWork, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isForFictionalWork Context triple: [Outstanding Supporting Actor in a Drama Series, isForFictionalWork, true]
-
A.
hasFictionalWork
Indicates that one entity is the creator, owner, or source of a fictional work associated with another entity.
-
B.
worksInFictionalContext
Indicates that an entity performs work or fulfills a role within a fictional or imagined setting rather than in real-world circumstances.
-
C.
hasFictionalType
Indicates that an entity is associated with or classified under a particular type or category that is fictional rather than real.
-
D.
worksWithInFiction
Indicates that two fictional characters are depicted as collaborating, interacting, or being associated with each other within a narrative work.
-
E.
isFictionalCharacter
Indicates that the subject is a character that exists only in fiction rather than in real life.
- 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_69f3496fdf0081908c1aa30870ce518b |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6e52a6d40819084472f6072c91e9f |
completed | May 3, 2026, 6:03 a.m. |
| PD | Predicate disambiguation | batch_69f6e3da41948190a4cfe866ce184f73 |
completed | May 3, 2026, 5:57 a.m. |
| PDg | Predicate description generation | batch_69f6e47c13348190a10528c84a401178 |
completed | May 3, 2026, 6 a.m. |
Created at: May 1, 2026, 1:36 a.m.