Triple
T17971261
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Betty Before X |
E449344
|
entity |
| Predicate | hasAuthorRelationToSubject |
P36598
|
FINISHED |
| Object | Ilyasah Shabazz is the daughter of Betty Shabazz |
—
|
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: Ilyasah Shabazz is the daughter of Betty Shabazz | Statement: [Betty Before X, hasAuthorRelationToSubject, Ilyasah Shabazz is the daughter of Betty Shabazz]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAuthorRelationToSubject Context triple: [Betty Before X, hasAuthorRelationToSubject, Ilyasah Shabazz is the daughter of Betty Shabazz]
-
A.
hasAuthorRelationshipToSubject
chosen
Indicates that an entity serves as the author or creator of the specified subject.
-
B.
hasAuthorRelationship
Indicates a relationship where one entity serves as the author or creator of another entity (such as a work, document, or resource).
-
C.
hasAuthor
Indicates that an entity is written or created by a specific author.
-
D.
hasAuthorOf
Indicates that one entity is the author or creator of another entity (such as a work, document, or publication).
-
E.
authorRelationshipToMainSubject
Indicates the nature of the connection or role the author has in relation to the main subject.
- 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_69d8b9f9927c8190a006110c8b996e61 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4b1fa67c48190936f20cea45e4599 |
completed | April 19, 2026, 10:44 a.m. |
| PD | Predicate disambiguation | batch_69e3f8fa62688190a5d5c361ab896256 |
completed | April 18, 2026, 9:34 p.m. |
Created at: April 10, 2026, 10:22 a.m.