Triple
T31917897
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shinobu Oshino |
E814884
|
entity |
| Predicate | wasTurnedIntoVampireBy |
P172729
|
FINISHED |
| Object | unknown vampire |
—
|
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: unknown vampire | Statement: [Shinobu Oshino, wasTurnedIntoVampireBy, unknown vampire]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wasTurnedIntoVampireBy Context triple: [Shinobu Oshino, wasTurnedIntoVampireBy, unknown vampire]
-
A.
turnedIntoVampireIn
Indicates that an entity became a vampire at or within a specified place or context.
-
B.
turnedIntoWerewolfBy
Indicates that one entity was transformed into a werewolf as a result of the actions or influence of another entity.
-
C.
turnedIntoVampireAtAge
Indicates that one entity became a vampire when it reached a specified age.
-
D.
vampireType
Indicates that one entity is classified as a specific type or category of vampire in relation to another entity.
-
E.
becameSire
Indicates that one entity has fathered offspring with another, thereby becoming its sire.
- 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_69f348f109d88190b5005372c53d2fcd |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b1f1dc708190a95613f030b963ae |
completed | May 3, 2026, 2:24 a.m. |
| PD | Predicate disambiguation | batch_69f6aca59d4881908d14ed47962703bd |
completed | May 3, 2026, 2:02 a.m. |
| PDg | Predicate description generation | batch_69f6af7d92008190aead47eaae8cc091 |
completed | May 3, 2026, 2:14 a.m. |
Created at: May 1, 2026, 12:02 a.m.