Triple
T652098
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ezra–Nehemiah |
E11365
|
entity |
| Predicate | literaryFeature |
P16928
|
FINISHED |
| Object | first-person memoir sections |
—
|
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: first-person memoir sections | Statement: [Ezra–Nehemiah, literaryFeature, first-person memoir sections]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: literaryFeature Context triple: [Ezra–Nehemiah, literaryFeature, first-person memoir sections]
-
A.
literaryLanguage
Indicates that an entity is expressed, written, or communicated using a particular literary or standardized written language.
-
B.
hasLiterarySignificance
Indicates that something holds notable importance, influence, or value within the realm of literature or literary studies.
-
C.
literarySource
Indicates that one entity serves as the written or literary origin, reference, or basis for another entity.
-
D.
linguisticFeature
Indicates a relationship where a linguistic property, pattern, or characteristic is attributed to or associated with a language-related entity (such as a word, phrase, or text).
-
E.
literaryRole
Indicates the specific narrative or functional role an entity holds within a literary work or text.
- 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_69a493266a2881909daf4c40f719dee8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f35acb08190a3a8248023ce07f9 |
completed | March 1, 2026, 8:19 p.m. |
| PD | Predicate disambiguation | batch_69a49d1001088190aa7ca3c8f2ad0e32 |
completed | March 1, 2026, 8:09 p.m. |
| PDg | Predicate description generation | batch_69a49dc0e6a08190b81d82a6f2571c41 |
completed | March 1, 2026, 8:12 p.m. |
Created at: March 1, 2026, 7:36 p.m.