Triple
T11997727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catholic Boy |
E285572
|
entity |
| Predicate | featuresSpokenWordInfluence |
P36061
|
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: [Catholic Boy, featuresSpokenWordInfluence, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresSpokenWordInfluence Context triple: [Catholic Boy, featuresSpokenWordInfluence, true]
-
A.
vocalInfluence
Indicates that one entity affects, shapes, or modifies another entity through vocal expression, such as speech, tone, or sound.
-
B.
hasSpokenStandardInfluence
Indicates that one entity has exerted a recognized or normative influence through spoken communication on another entity or context.
-
C.
influencedDiscussionOf
Indicates that one entity had an effect on the way another entity was discussed, framed, or debated.
-
D.
includesSpokenWordAppearanceBy
chosen
Indicates that something (such as a work, recording, or event) contains an instance where a person or entity appears through spoken words.
-
E.
influentialFrom
Indicates that one entity has exerted influence on another, contributing to or shaping the latter’s ideas, behavior, or development.
- 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_69d6ab44a77c8190a652f4b27164e4ef |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903c172788190b92042e9d10a48bf |
completed | April 10, 2026, 2:05 p.m. |
| PD | Predicate disambiguation | batch_69d902b245cc8190af96a9c2bd9c6250 |
completed | April 10, 2026, 2:01 p.m. |
Created at: April 8, 2026, 9:46 p.m.