Triple
T16841571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Borchester |
E409424
|
entity |
| Predicate | hasFictionalAmenity |
P58963
|
FINISHED |
| Object | shops |
—
|
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: shops | Statement: [Borchester, hasFictionalAmenity, shops]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalAmenity Context triple: [Borchester, hasFictionalAmenity, shops]
-
A.
hasFictionalLandmark
Indicates that one entity includes, features, or is associated with a landmark that is fictional rather than real.
-
B.
hasFictionalProperty
chosen
Indicates that an entity possesses a property, attribute, or characteristic that exists only in a fictional or imaginary context.
-
C.
hasFictionalFunction
Indicates that an entity serves a role, purpose, or function within a fictional context or narrative.
-
D.
hasFictionalEventType
Indicates that something is associated with, characterized by, or classified under a particular type or category of fictional event.
-
E.
hasFictionalSettingElement
Indicates that something includes or is associated with a specific element or component of a fictional setting.
- 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_69d883952b048190887740a980b712ed |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b35167a48190b45a459023e3ab1b |
completed | April 18, 2026, 4:37 p.m. |
| PD | Predicate disambiguation | batch_69e32b87b4248190aaddb05e88452356 |
completed | April 18, 2026, 6:58 a.m. |
Created at: April 10, 2026, 5:24 a.m.