Triple
T24899285
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | At-Tahrim |
E623529
|
entity |
| Predicate | givesExampleOf |
P41975
|
FINISHED |
| Object | righteous women |
—
|
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: righteous women | Statement: [At-Tahrim, givesExampleOf, righteous women]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: givesExampleOf Context triple: [At-Tahrim, givesExampleOf, righteous women]
-
A.
hasExample
Indicates that one entity serves as an instance, illustration, or concrete example of another entity.
-
B.
isCanonicalExampleOf
Indicates that something serves as a standard or typical instance that exemplifies a concept, category, or pattern.
-
C.
usedAsExampleIn
chosen
Indicates that one entity is cited or presented as an illustrative example within another entity, such as a text, discussion, or explanation.
-
D.
baseExamples
Indicates that something serves as a fundamental or illustrative example for understanding or demonstrating another concept, item, or case.
-
E.
extraExample
Indicates that something is provided as an additional, illustrative instance beyond the main or required examples.
- 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_69e2fac797cc8190b30d77f4121099ac |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f44a417a58819081777e18dda149fd |
completed | May 1, 2026, 6:37 a.m. |
| PD | Predicate disambiguation | batch_69f442b8479c8190a7c8e416ac9e28a0 |
completed | May 1, 2026, 6:05 a.m. |
Created at: April 18, 2026, 5:26 a.m.