Triple
T2481115
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Surah At-Tahrim |
E55816
|
entity |
| Predicate | containsExampleOf |
P1259
|
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: [Surah At-Tahrim, containsExampleOf, righteous women]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsExampleOf Context triple: [Surah At-Tahrim, containsExampleOf, righteous women]
-
A.
hasExample
chosen
Indicates that one entity serves as an instance, illustration, or concrete example of another entity.
-
B.
hasNonExample
Indicates that something is associated with an instance that explicitly does not satisfy or illustrate a given concept, rule, or category.
-
C.
nonExample
Indicates that something is explicitly identified as not being an example or instance of a given concept, category, or pattern.
-
D.
notableExampleAt
Indicates that something serves as a prominent or illustrative example of something else in a particular context or location.
-
E.
includesExampleTaxon
Indicates that a taxonomic group or concept contains a specific taxon used as an illustrative or representative example.
- 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_69ab49e670a88190b928e08302381710 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd20b6d008190acec0eb172e218c9 |
completed | March 7, 2026, 7:21 a.m. |
| PD | Predicate disambiguation | batch_69abd0b7cf088190bcff4dac6150044c |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:45 p.m.