Triple
T1463900
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zaynab bint Muhammad |
E31574
|
entity |
| Predicate | lifeRole |
P268
|
FINISHED |
| Object | companionOfProphetMuhammad |
—
|
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: companionOfProphetMuhammad | Statement: [Zaynab bint Muhammad, lifeRole, companionOfProphetMuhammad]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lifeRole Context triple: [Zaynab bint Muhammad, lifeRole, companionOfProphetMuhammad]
-
A.
role
chosen
Indicates the function, position, or responsibility that one entity holds in relation to another within a given context.
-
B.
roleInText
Indicates that an entity participates in a text with a specific function or capacity (e.g., author, editor, character).
-
C.
urbanRole
Indicates the function, status, or role that an entity holds within an urban or city context.
-
D.
typicalRole
Indicates that one entity serves as the usual, characteristic, or commonly expected role or function of another entity.
-
E.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
- 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_69a49917dfc081909acdbdf5d684f1ef |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c5b89708819084fb9ba4ff293b8b |
completed | March 1, 2026, 11:03 p.m. |
| PD | Predicate disambiguation | batch_69a4c48121e48190946c23c583e5fb64 |
completed | March 1, 2026, 10:58 p.m. |
Created at: March 1, 2026, 8 p.m.