Triple
T20416911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Spotted Cat and Other Mysteries from Inspector Cockrill's Casebook |
E500735
|
entity |
| Predicate | hasSleuthType |
P16808
|
FINISHED |
| Object | professional police inspector |
—
|
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: professional police inspector | Statement: [The Spotted Cat and Other Mysteries from Inspector Cockrill's Casebook, hasSleuthType, professional police inspector]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSleuthType Context triple: [The Spotted Cat and Other Mysteries from Inspector Cockrill's Casebook, hasSleuthType, professional police inspector]
-
A.
haveType
chosen
Indicates that an entity belongs to or is classified under a specified type or category.
-
B.
hasThiefCharacter
Indicates that an entity includes or features a character whose role or identity is that of a thief.
-
C.
hasTrickType
Indicates that an entity (such as a trick or maneuver) is associated with a specific type or category of trick.
-
D.
hasTypeOfSecrecy
Indicates that something is associated with a particular kind or level of secrecy.
-
E.
hasDiscoveryType
Indicates the specific manner, method, or category by which something was discovered.
- 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_69e0b4a935588190b9446a99b37ced44 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e67a4437448190b07b6e6e3de5830f |
completed | April 20, 2026, 7:11 p.m. |
| PD | Predicate disambiguation | batch_69e5766df0008190a73c4f613c29678f |
completed | April 20, 2026, 12:42 a.m. |
Created at: April 16, 2026, 11:30 a.m.