Triple
T24205852
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bludleigh Court |
E600102
|
entity |
| Predicate | featuredDetective |
P63938
|
FINISHED |
| Object | Albert Campion |
—
|
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: Albert Campion | Statement: [Bludleigh Court, featuredDetective, Albert Campion]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuredDetective Context triple: [Bludleigh Court, featuredDetective, Albert Campion]
-
A.
fictionalDetective
Indicates that the subject is a detective character who exists only in fiction rather than in real life.
-
B.
detectiveType
Indicates that one entity is classified as a particular type or category of detective in relation to another entity.
-
C.
featuresPrivateDetective
Indicates that the subject includes or involves a private detective as a notable element or character.
-
D.
featuresDetectiveDuo
Indicates that the subject involves or centers around a pair of detectives working together as a team.
-
E.
hasFictionalDetective
chosen
Indicates that one entity (typically a work or series) features or includes a fictional detective character as part of its content.
- 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_69e288ceaab88190899d0acb5931591d |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f27ca63d188190add6c41929bb5cb5 |
completed | April 29, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69f1c43e55688190b55fc20274ed471c |
completed | April 29, 2026, 8:41 a.m. |
Created at: April 17, 2026, 11:37 p.m.