Triple
T34062174
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ezra Chater |
E873519
|
entity |
| Predicate | literaryAttribute |
P16928
|
FINISHED |
| Object | comically pompous |
—
|
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: comically pompous | Statement: [Ezra Chater, literaryAttribute, comically pompous]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: literaryAttribute Context triple: [Ezra Chater, literaryAttribute, comically pompous]
-
A.
literaryFeature
chosen
Indicates a relationship where something possesses or exhibits a characteristic, device, or stylistic element used in literature.
-
B.
literarySubject
Indicates that one entity serves as the subject, topic, or focus of a literary work created by another entity.
-
C.
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.
-
D.
literaryAuthor
Indicates that one entity is the author or writer of a literary work represented by the other entity.
-
E.
literaryLanguage
Indicates that an entity is expressed, written, or communicated using a particular literary or standardized written language.
- 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_69f349a4af208190afa14888f9c9fb9d |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f7c777e924819081a6634f549fe552 |
completed | May 3, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69f7c475c58c8190a883554231e88c88 |
completed | May 3, 2026, 9:56 p.m. |
Created at: May 1, 2026, 1:52 a.m.