Triple
T16841448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Borsetshire |
E409421
|
entity |
| Predicate | hasFictionalAgriculturalCharacter |
P125067
|
FINISHED |
| Object | dairy farming |
—
|
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: dairy farming | Statement: [Borsetshire, hasFictionalAgriculturalCharacter, dairy farming]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalAgriculturalCharacter Context triple: [Borsetshire, hasFictionalAgriculturalCharacter, dairy farming]
-
A.
isFictionalCharacter
Indicates that the subject is a character that exists only in fiction rather than in real life.
-
B.
fictionalCharacter
Indicates that one entity is a fictional character that appears within the narrative world of another entity (such as a work, series, or franchise).
-
C.
hasFairyCharacters
Indicates that the subject includes or features characters that are fairies.
-
D.
meetsFictionalCharacter
Indicates that one entity encounters or comes into contact with a fictional character.
-
E.
hasFictionalType
Indicates that an entity is associated with or classified under a particular type or category that is fictional rather than real.
- F. None of above. chosen
Provenance (4 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. |
| PDg | Predicate description generation | batch_69e34fb7c8c8819086975b7955b7d8ef |
completed | April 18, 2026, 9:32 a.m. |
Created at: April 10, 2026, 5:24 a.m.