Triple
T16239080
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Imagination and Fancy |
E394191
|
entity |
| Predicate | usesExamplesFrom |
P41975
|
FINISHED |
| Object | English 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: English poetry | Statement: [Imagination and Fancy, usesExamplesFrom, English poetry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesExamplesFrom Context triple: [Imagination and Fancy, usesExamplesFrom, English poetry]
-
A.
usedAsExampleIn
chosen
Indicates that one entity is cited or presented as an illustrative example within another entity, such as a text, discussion, or explanation.
-
B.
hasExample
Indicates that one entity serves as an instance, illustration, or concrete example of another entity.
-
C.
baseExamples
Indicates that something serves as a fundamental or illustrative example for understanding or demonstrating another concept, item, or case.
-
D.
includesExamplesSuchAs
Indicates that one entity provides specific instances or samples that illustrate or clarify another entity.
-
E.
toolUseExamples
Indicates that one entity provides example instances or demonstrations of how a particular tool is or can be used by another entity.
- 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_69d87f2171208190951025e526947816 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2455c7a3c81909e3b42edf03be43e |
completed | April 17, 2026, 2:36 p.m. |
| PD | Predicate disambiguation | batch_69e219ee6f6481909663b388dc99770a |
completed | April 17, 2026, 11:30 a.m. |
Created at: April 10, 2026, 5:04 a.m.