Triple

T17071250
Position Surface form Disambiguated ID Type / Status
Subject China Moses E414225 entity
Predicate name P16 FINISHED
Object China Moses E414225 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: China Moses | Statement: [China Moses, name, China Moses]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: China Moses
Context triple: [China Moses, name, China Moses]
  • A. China Moses chosen
    China Moses is an American-born jazz and soul singer, television host, and producer known for her rich vocals and work bridging classic jazz with contemporary styles.
  • B. Moses ǁGaroëb
    Moses ǁGaroëb was a prominent Namibian politician and anti-apartheid activist who played a key role in the country’s struggle for independence.
  • C. Moses the Black
    Moses the Black was a 4th-century Ethiopian desert monk and former bandit who became a renowned Christian ascetic and saint among the Desert Fathers.
  • D. Moses Blah
    Moses Blah was a Liberian politician and former vice president who briefly served as Liberia’s president in 2003 during the turbulent final phase of the Second Liberian Civil War.
  • E. Moses Pray
    Moses Pray is a charmingly roguish Bible salesman and con man who becomes the reluctant guardian and partner-in-crime of a young girl in the film and novel "Paper Moon."
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69d886cef44c8190ba56c44b4e863e64 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3dbc0982c8190916f9905ddb5e575 completed April 18, 2026, 7:30 p.m.
NED1 Entity disambiguation (via context triple) batch_6a012edbce988190a784448ba8a258a5 completed May 11, 2026, 1:20 a.m.
Created at: April 10, 2026, 5:34 a.m.