Triple
T31481504
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abu al-Husayn |
E803155
|
entity |
| Predicate | associatedPersonProfession |
P83547
|
FINISHED |
| Object | muhaddith |
—
|
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: muhaddith | Statement: [Abu al-Husayn, associatedPersonProfession, muhaddith]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedPersonProfession Context triple: [Abu al-Husayn, associatedPersonProfession, muhaddith]
-
A.
memberProfession
Indicates that a member or individual holds or practices a particular profession or occupation.
-
B.
isAssociatedWithProfessionOfBearer
Indicates that one entity is connected to, or involved with, the profession or occupational role held by another entity.
-
C.
occupationOfAssociatedPerson
chosen
Indicates the job or professional role held by a person who is associated with another referenced entity.
-
D.
relatedProfession
Indicates that two entities have professions that are connected or associated in some meaningful way, such as being in the same field, industry, or professional domain.
-
E.
ownerProfession
Indicates that the profession or occupation is associated with, or held by, the owner of a specified entity.
- 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_69f348c9477c8190bc0a21f6d482d2fc |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a037c876524819098545e6037d3107d |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379e7aa0c8190bdc9ee4d54fc821b |
completed | May 12, 2026, 7:05 p.m. |
Created at: April 30, 2026, 9:32 p.m.