Triple
T25007669
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Problem of Susan |
E625890
|
entity |
| Predicate | hasIllustratedAdaptation |
P2386
|
FINISHED |
| Object | The Problem of Susan and Other Stories (2019) |
—
|
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: The Problem of Susan and Other Stories (2019) | Statement: [The Problem of Susan, hasIllustratedAdaptation, The Problem of Susan and Other Stories (2019)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasIllustratedAdaptation Context triple: [The Problem of Susan, hasIllustratedAdaptation, The Problem of Susan and Other Stories (2019)]
-
A.
hasGraphicNovelAdaptation
Indicates that a work has been adapted into a graphic novel format.
-
B.
hasIllustratedCharacters
Indicates that something includes or features characters that are depicted through illustrations.
-
C.
oftenIllustratedBy
Indicates that something is frequently depicted, represented, or exemplified through a particular image, example, or illustration.
-
D.
hasIllustrations
chosen
Indicates that an entity includes or is accompanied by visual illustrations.
-
E.
hasNotableIllustrationsOf
Indicates that something contains or features particularly significant or noteworthy illustrations depicting another entity or subject.
- 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_69e2ff26c50481908bc82e799c9e6587 |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f44b12bd788190bc32bb8129c4550e |
completed | May 1, 2026, 6:41 a.m. |
| PD | Predicate disambiguation | batch_69f442c0c2e88190acd7f170f10ccef6 |
completed | May 1, 2026, 6:05 a.m. |
Created at: April 18, 2026, 6:05 a.m.