Triple
T7229408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abu Lahab |
E154864
|
entity |
| Predicate | hasWifeMentionedInQuran |
P75744
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Abu Lahab, hasWifeMentionedInQuran, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWifeMentionedInQuran Context triple: [Abu Lahab, hasWifeMentionedInQuran, yes]
-
A.
wasMarriedBeforeProphet
Indicates that a person had been married at least once prior to the time of becoming a prophet.
-
B.
coWifeOf
Indicates that two women are married to the same spouse at the same time, making them co-wives in a polygamous marriage.
-
C.
spouseAssociatedWith
Indicates a marital or spousal relationship or close association between two entities.
-
D.
hasAuthorSpouse
Indicates that the spouse of the subject entity is the author of the related work or entity.
-
E.
marriageToMuhammadType
Indicates a marital relationship in which the person is (or was) married to Muhammad, specifying that the marriage is to the individual identified as Muhammad.
- F. None of above. chosen
Provenance (4 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_69c68811dd1c8190ac460bb39e64e1f0 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ea0d9b6c8190a0b5f0ab8d5cca19 |
completed | March 27, 2026, 8:35 p.m. |
| PD | Predicate disambiguation | batch_69c6e761b7fc8190857794d78af1b468 |
completed | March 27, 2026, 8:24 p.m. |
| PDg | Predicate description generation | batch_69c6e8b5f6508190af28e06a7959d717 |
completed | March 27, 2026, 8:29 p.m. |
Created at: March 27, 2026, 2:54 p.m.