Triple

T2854446
Position Surface form Disambiguated ID Type / Status
Subject Song to Song E63166 entity
Predicate producedBy P490 FINISHED
Object Ken Kao
Ken Kao is an American film producer known for backing a range of independent and auteur-driven projects.
E305281 NE FINISHED

How this triple was built (4 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: Ken Kao | Statement: [Song to Song, producedBy, Ken Kao]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ken Kao
Context triple: [Song to Song, producedBy, Ken Kao]
  • A. Kenneth Hsu
    Kenneth Hsu is a Swiss geologist and oceanographer known for his influential work on marine geology and the Messinian salinity crisis.
  • B. John Cheng
    John Cheng is a film producer best known for his work on the dark comedy movie "Horrible Bosses."
  • C. Johnny Chiang
    Johnny Chiang is a Taiwanese politician who has served as a prominent leader within the Kuomintang (KMT) party and as a legislator in Taiwan’s Legislative Yuan.
  • D. Daren Tang
    Daren Tang is a Singaporean lawyer and intellectual property expert who serves as the Director General of the World Intellectual Property Organization (WIPO).
  • E. Yu-Chi Ho
    Yu-Chi Ho is a prominent control theorist and engineer known for his pioneering contributions to optimal control, dynamic systems, and game theory.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Ken Kao
Triple: [Song to Song, producedBy, Ken Kao]
Generated description
Ken Kao is an American film producer known for backing a range of independent and auteur-driven projects.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Ken Kao
Target entity description: Ken Kao is an American film producer known for backing a range of independent and auteur-driven projects.
  • A. Kenneth Hsu
    Kenneth Hsu is a Swiss geologist and oceanographer known for his influential work on marine geology and the Messinian salinity crisis.
  • B. John Cheng
    John Cheng is a film producer best known for his work on the dark comedy movie "Horrible Bosses."
  • C. Johnny Chiang
    Johnny Chiang is a Taiwanese politician who has served as a prominent leader within the Kuomintang (KMT) party and as a legislator in Taiwan’s Legislative Yuan.
  • D. Daren Tang
    Daren Tang is a Singaporean lawyer and intellectual property expert who serves as the Director General of the World Intellectual Property Organization (WIPO).
  • E. Yu-Chi Ho
    Yu-Chi Ho is a prominent control theorist and engineer known for his pioneering contributions to optimal control, dynamic systems, and game theory.
  • F. None of above. chosen

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_69ab4c407c408190857d25e027155ce9 completed March 6, 2026, 9:50 p.m.
NER Named-entity recognition batch_69abdf60852c8190b66c8719c63a723e completed March 7, 2026, 8:18 a.m.
NED1 Entity disambiguation (via context triple) batch_69b01d8774cc8190aa6ed40b26c4a568 completed March 10, 2026, 1:32 p.m.
NEDg Description generation batch_69b01ea4e3a481909241383e2c4093d8 completed March 10, 2026, 1:37 p.m.
NED2 Entity disambiguation (via description) batch_69b01f1a9ac48190b8d2b5247cb2c3d8 completed March 10, 2026, 1:39 p.m.
Created at: March 6, 2026, 10:02 p.m.