Triple

T36347787
Position Surface form Disambiguated ID Type / Status
Subject The Rebel Princess E895111 entity
Predicate starredActor P5563 FINISHED
Object Zhang Tianyang
Zhang Tianyang is a Chinese actor known for his role in the historical drama series "The Rebel Princess."
E2189254 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: Zhang Tianyang | Statement: [The Rebel Princess, starredActor, Zhang Tianyang]
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: Zhang Tianyang
Triple: [The Rebel Princess, starredActor, Zhang Tianyang]
Generated description
Zhang Tianyang is a Chinese actor known for his role in the historical drama series "The Rebel Princess."

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_69f76e4f437c8190a1af3ea2564f41f5 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7baa23fd08190859bc334c5b3b0c6 completed May 3, 2026, 9:14 p.m.
NED1 Entity disambiguation (via context triple) batch_6a39e6c690008190972104e81a27abbb completed June 23, 2026, 1:52 a.m.
NEDg Description generation batch_6a39ec063b0c81908b0c23c3079904eb completed June 23, 2026, 2:14 a.m.
NED2 Entity disambiguation (via description) batch_6a39ed5773d08190b15416e6186ceaef completed June 23, 2026, 2:20 a.m.
Created at: May 3, 2026, 4:09 p.m.