Triple

T30346539
Position Surface form Disambiguated ID Type / Status
Subject Gen Urobuchi E771881 entity
Predicate awardReceived P11 FINISHED
Object Tokyo Anime Award for Best Script
The Tokyo Anime Award for Best Script is a prestigious Japanese anime industry honor recognizing outstanding screenwriting and series composition in animated works.
E1910866 NE 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: Tokyo Anime Award for Best Script | Statement: [Gen Urobuchi, awardReceived, Tokyo Anime Award for Best Script]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Tokyo Anime Award for Best Script
Triple: [Gen Urobuchi, awardReceived, Tokyo Anime Award for Best Script]
Generated description
The Tokyo Anime Award for Best Script is a prestigious Japanese anime industry honor recognizing outstanding screenwriting and series composition in animated works.

Provenance (5 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_69f2248b9a208190bc3e6804acd5afd6 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f682084db081909e261c5bbae024e3 completed May 2, 2026, 11 p.m.
NED1 Entity disambiguation (via context triple) batch_6a277c28f12c8190a703fc2c9b2fe97a completed June 9, 2026, 2:36 a.m.
NEDg Description generation batch_6a277cab7a848190b3bb869bb2f794fa completed June 9, 2026, 2:38 a.m.
NED2 Entity disambiguation (via description) batch_6a277d7238a481909b4eccfff1d10aa9 completed June 9, 2026, 2:41 a.m.
Created at: April 29, 2026, 7:56 p.m.