Triple
T22996823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Case of the Smoking Chimney |
E572519
|
entity |
| Predicate | featuresGenreCharacter |
P5483
|
FINISHED |
| Object | criminals |
—
|
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: criminals | Statement: [The Case of the Smoking Chimney, featuresGenreCharacter, criminals]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresGenreCharacter Context triple: [The Case of the Smoking Chimney, featuresGenreCharacter, criminals]
-
A.
featuresCharacterWith
Indicates that one entity (such as a work or product) includes or presents a particular character as part of its content.
-
B.
genreFeatures
chosen
Indicates that a particular genre is characterized or defined by certain features or attributes.
-
C.
featuresCharactersFrom
Indicates that one entity (such as a work or production) includes or presents characters originating from another entity.
-
D.
featuresFictionalMusical
Indicates that a work includes or prominently involves a fictional musical as part of its content or storyline.
-
E.
featuresCastType
Indicates that one entity includes or highlights a particular type or category of cast (e.g., actors or performers) associated with it.
- 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_69e245b6a3ac81908087599eefe3e365 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f182f452b48190951fc5dde56c1bb2 |
completed | April 29, 2026, 4:03 a.m. |
| PD | Predicate disambiguation | batch_69ef3b974e7c8190b8be11dbb4518693 |
completed | April 27, 2026, 10:33 a.m. |
Created at: April 17, 2026, 3:50 p.m.