Triple

T17155864
Position Surface form Disambiguated ID Type / Status
Subject The Return of Godzilla E416342 entity
Predicate stars P1956 FINISHED
Object Yosuke Natsuki
Yosuke Natsuki was a Japanese actor known for his roles in genre films and television dramas, including entries in the Godzilla franchise.
E1689460 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: Yosuke Natsuki | Statement: [The Return of Godzilla, stars, Yosuke Natsuki]
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: Yosuke Natsuki
Triple: [The Return of Godzilla, stars, Yosuke Natsuki]
Generated description
Yosuke Natsuki was a Japanese actor known for his roles in genre films and television dramas, including entries in the Godzilla franchise.

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_69d886d279c081909f8ff1f743ddeb69 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3f40b402881908b8c01d7b957d0d2 completed April 18, 2026, 9:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10c0fa870c81909ae631e459d1737f completed May 22, 2026, 8:47 p.m.
NEDg Description generation batch_6a10c2d522988190bc01978dc5ef272f completed May 22, 2026, 8:55 p.m.
NED2 Entity disambiguation (via description) batch_6a10c36b20a0819084d94066362937ee completed May 22, 2026, 8:58 p.m.
Created at: April 10, 2026, 5:37 a.m.