Triple
T31105872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Three Times |
E792789
|
entity |
| Predicate | featuresCharacterPairReusedAcrossSegments |
P91997
|
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: [Three Times, featuresCharacterPairReusedAcrossSegments, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresCharacterPairReusedAcrossSegments Context triple: [Three Times, featuresCharacterPairReusedAcrossSegments, yes]
-
A.
hasRecurringCharacterFrom
Indicates that one work or series includes a character who also appears recurrently in another work or series.
-
B.
reusedIn
chosen
Indicates that something previously used in one context or instance is used again in another context or instance.
-
C.
usesRepetition
Indicates that one entity employs repeated elements, actions, or patterns as a deliberate feature or technique in relation to another entity or context.
-
D.
appearsInSegmentOf
Indicates that one entity occurs within, or is featured as part of, a specific segment or subsection of another entity.
-
E.
hasRecurringCharacters
Indicates that an entity (such as a work or series) features characters who appear repeatedly across multiple parts, episodes, or installments.
- 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_69f224cfd5d881908ec6447bc321cd58 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a004fd3caf48190b2ec063a7bf0756b |
completed | May 10, 2026, 9:28 a.m. |
| PD | Predicate disambiguation | batch_6a004f7672dc8190aca91d1ed855bf9a |
completed | May 10, 2026, 9:27 a.m. |
Created at: April 29, 2026, 9:03 p.m.