Triple
T38236778
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ezekiel 40 |
E1013638
|
entity |
| Predicate | literaryUnitWith |
P18697
|
FINISHED |
| Object | Ezekiel 41 |
—
|
NE NERFINISHED |
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: Ezekiel 41 | Statement: [Ezekiel 40, literaryUnitWith, Ezekiel 41]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: literaryUnitWith Context triple: [Ezekiel 40, literaryUnitWith, Ezekiel 41]
-
A.
literaryUnit
chosen
Indicates that one entity is a distinct segment or component (such as a chapter, scene, or passage) within a larger literary work or text.
-
B.
literaryCenter
Indicates that a location functions as a primary hub or focal point for literary activity, such as writing, publishing, or literary culture.
-
C.
literaryWorkInStory
Indicates that one literary work is referenced, featured, or embedded within the narrative of another story.
-
D.
literaryCollection
Indicates that one entity is a collection or compilation of literary works that includes or is associated with the other entity.
-
E.
usesLiteraryLens
Indicates that one entity analyzes, interprets, or evaluates another entity (such as a text or work) through a specific literary lens or critical framework.
- 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_69f76dd72a248190a5fe18db2bd1eb15 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fd09840ea88190a2e6d7e577ade717 |
completed | May 7, 2026, 9:52 p.m. |
| PD | Predicate disambiguation | batch_69fd064c49988190afadddbd04d7cb94 |
completed | May 7, 2026, 9:38 p.m. |
Created at: May 3, 2026, 4:30 p.m.