Triple
T13404350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Temple of Apollo Thymbraeus |
E319913
|
entity |
| Predicate | associatedWithLiteraryGenre |
P22130
|
FINISHED |
| Object | epic poetry |
—
|
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: epic poetry | Statement: [Temple of Apollo Thymbraeus, associatedWithLiteraryGenre, epic poetry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithLiteraryGenre Context triple: [Temple of Apollo Thymbraeus, associatedWithLiteraryGenre, epic poetry]
-
A.
literaryGenreOfWork
chosen
Indicates that a work belongs to or is classified under a particular literary genre.
-
B.
literaryGenreOfSourceWork
Indicates that a work belongs to, or is characterized by, a particular literary genre.
-
C.
hasLiteraryConnection
Indicates a relationship in which one entity is connected to another through a literary link, such as authorship, reference, influence, adaptation, or shared appearance in written works.
-
D.
literarySubject
Indicates that one entity serves as the subject, topic, or focus of a literary work created by another entity.
-
E.
literaryThemeInvolvement
Indicates the involvement or presence of a particular literary theme within a work, passage, or character arc.
- 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_69d806b943cc8190b6af624d385d7e12 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbae4ae47081909b68a9aaa62fd4c7 |
completed | April 12, 2026, 2:38 p.m. |
| PD | Predicate disambiguation | batch_69d9a0355de48190bb3fb96912e20df3 |
completed | April 11, 2026, 1:13 a.m. |
Created at: April 9, 2026, 9:34 p.m.