Triple
T30698862
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kinoko Nasu |
E781552
|
entity |
| Predicate | wroteScenarioFor |
P137164
|
FINISHED |
| Object | Fate/Grand Order |
—
|
NE NERFINISHED |
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: Fate/Grand Order | Statement: [Kinoko Nasu, wroteScenarioFor, Fate/Grand Order]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wroteScenarioFor Context triple: [Kinoko Nasu, wroteScenarioFor, Fate/Grand Order]
-
A.
narrativeSituation
Indicates the contextual relationship that defines how events, characters, and perspectives are arranged and presented within a narrative.
-
B.
narrativeDesigner
chosen
Indicates a relationship where an entity serves as the creator or architect of a story’s structure, dialogue, and interactive narrative elements for another entity such as a game, experience, or project.
-
C.
scenarioType
Indicates the specific category or kind of situation, context, or use case that an entity or event is associated with.
-
D.
wroteIn
Indicates that an entity authored or composed something using a particular language, medium, or writing system.
-
E.
narrativePremise
Indicates the foundational situation, conflict, or setup that initiates and drives the narrative’s events.
- 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_69f224ab24e08190991d6edb6df58e8b |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f68bdceb8c8190b78e499ea2357e0f |
completed | May 2, 2026, 11:42 p.m. |
| PD | Predicate disambiguation | batch_69f6861170d08190bb98be609d436f84 |
completed | May 2, 2026, 11:17 p.m. |
Created at: April 29, 2026, 8:34 p.m.