Triple
T12773689
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Godfrey Cass |
E305312
|
entity |
| Predicate | livesInFictionalCounty |
P47688
|
FINISHED |
| Object | Warwickshire-like rural England |
—
|
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: Warwickshire-like rural England | Statement: [Godfrey Cass, livesInFictionalCounty, Warwickshire-like rural England]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: livesInFictionalCounty Context triple: [Godfrey Cass, livesInFictionalCounty, Warwickshire-like rural England]
-
A.
hasFictionalCounty
Indicates that one entity includes, is set in, or is associated with a county that is fictional rather than real.
-
B.
hasFictionalCountySeatRole
Indicates that an entity serves in the role of county seat within a fictional or imaginary administrative setting.
-
C.
residesInFictionalLocation
chosen
Indicates that an entity lives or is based in a location that is explicitly fictional or imaginary.
-
D.
locatedInFictionalCountry
Indicates that an entity exists or is situated within a country that is fictional rather than real.
-
E.
basedInFictionalLocation
Indicates that an entity’s primary setting, origin, or operations occur in a fictional (non-real) location.
- 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_69d7bdf2b43c819098ae5aa68e61ea58 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96df5b68481908a5d40516b09be52 |
completed | April 10, 2026, 9:39 p.m. |
| PD | Predicate disambiguation | batch_69d96409739881909174ba005a986cb5 |
completed | April 10, 2026, 8:56 p.m. |
Created at: April 9, 2026, 5:28 p.m.