Triple
T20544034
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Gideon Fell series |
E504417
|
entity |
| Predicate | mainDetectiveDescription |
P80419
|
FINISHED |
| Object | eccentric and scholarly sleuth |
—
|
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: eccentric and scholarly sleuth | Statement: [Dr. Gideon Fell series, mainDetectiveDescription, eccentric and scholarly sleuth]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainDetectiveDescription Context triple: [Dr. Gideon Fell series, mainDetectiveDescription, eccentric and scholarly sleuth]
-
A.
detectiveType
chosen
Indicates that one entity is classified as a particular type or category of detective in relation to another entity.
-
B.
portrayedDetective
Indicates that one entity has played or depicted a detective character in a performance or work.
-
C.
hasClericalDetective
Indicates that an entity includes or is associated with a detective who is also a member of the clergy.
-
D.
featuresDetectiveDuo
Indicates that the subject involves or centers around a pair of detectives working together as a team.
-
E.
fictionalDetective
Indicates that the subject is a detective character who exists only in fiction rather than in real life.
- 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_69e0b4b476648190bc6019622ae54d3c |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a2957c308190aab81127f82f8aa6 |
completed | April 20, 2026, 10:03 p.m. |
| PD | Predicate disambiguation | batch_69e59fe5592c8190bb6122b784496d02 |
completed | April 20, 2026, 3:39 a.m. |
Created at: April 16, 2026, 11:38 a.m.