Triple
T31171623
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eddie O’Hare |
E794619
|
entity |
| Predicate | hasAuthorCharacterRelationshipWith |
P89076
|
FINISHED |
| Object | a famous children’s author |
—
|
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: a famous children’s author | Statement: [Eddie O’Hare, hasAuthorCharacterRelationshipWith, a famous children’s author]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAuthorCharacterRelationshipWith Context triple: [Eddie O’Hare, hasAuthorCharacterRelationshipWith, a famous children’s author]
-
A.
hasAuthorRelationship
Indicates a relationship where one entity serves as the author or creator of another entity (such as a work, document, or resource).
-
B.
hasAuthorRelationshipToSubject
Indicates that an entity serves as the author or creator of the specified subject.
-
C.
subjectRelationToAuthor
Indicates the relationship or connection that the subject has to the author.
-
D.
literaryRelationship
Indicates a relationship between entities that are connected through literature, such as authorship, influence, adaptation, or other text-based associations.
-
E.
hasAuthorCharacteristic
chosen
Indicates that an author possesses a particular attribute, trait, or quality.
- 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_69f224d5b9708190b6ca79ad2fd3a28a |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f79f48acec8190a9d5964581a94f6c |
completed | May 3, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_69f79e4888248190be2f63cdfb5cd7b7 |
completed | May 3, 2026, 7:13 p.m. |
Created at: April 29, 2026, 9:07 p.m.