Triple
T834531
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | In Evil Hour |
E18040
|
entity |
| Predicate | hasProtagonistCharacteristic |
P662
|
FINISHED |
| Object | moral ambiguity of town authorities |
—
|
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: moral ambiguity of town authorities | Statement: [In Evil Hour, hasProtagonistCharacteristic, moral ambiguity of town authorities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProtagonistCharacteristic Context triple: [In Evil Hour, hasProtagonistCharacteristic, moral ambiguity of town authorities]
-
A.
hasProtagonist
Indicates that a work of narrative has a main character who serves as its central focus or driving agent.
-
B.
mainProtagonist
Indicates that the subject is the central character or primary focus in the narrative of the related work.
-
C.
characterizedBy
chosen
Indicates that one entity possesses a defining quality, feature, or attribute expressed by another entity.
-
D.
protagonistAlterEgoOf
Indicates that one entity is the alternate identity or secret persona of the main character (protagonist) in a narrative.
-
E.
character1
Indicates that the subject is identified as the first or primary character in a narrative or context.
- 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_69a49389f44881909a608fb27d89f247 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4abccb94881909cd49aa3fd986b4a |
completed | March 1, 2026, 9:12 p.m. |
| PD | Predicate disambiguation | batch_69a4aa7c7df881909c539c3ab8ff0367 |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:38 p.m.