Triple

T7208418
Position Surface form Disambiguated ID Type / Status
Subject CMP languages E148733 entity
Predicate proposedBy P32 FINISHED
Object Robert Blust E138026 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: Robert Blust | Statement: [CMP languages, proposedBy, Robert Blust]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Robert Blust
Context triple: [CMP languages, proposedBy, Robert Blust]
  • A. Robert Blust chosen
    Robert Blust was an influential American linguist and Austronesian specialist known for his extensive comparative and historical work on the languages of the Pacific and Southeast Asia.
  • B. James A. Matisoff
    James A. Matisoff is an American linguist renowned for his pioneering work on Sino-Tibetan historical linguistics and language reconstruction.
  • C. Paul Kiparsky
    Paul Kiparsky is a prominent linguist known for his influential work in generative phonology and historical linguistics.
  • D. Edwin G. Pulleyblank
    Edwin G. Pulleyblank was a prominent Canadian sinologist and historical linguist known for his influential reconstructions of Middle Chinese phonology and major contributions to the study of Chinese historical linguistics.
  • E. Martin Haspelmath
    Martin Haspelmath is a prominent German linguist known for his influential work in linguistic typology, grammatical description, and the development of cross-linguistic databases.
  • 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_69c687e8cf188190b5f3ecffd681f04e completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6e96ae4dc8190b0b9e064ff968c10 completed March 27, 2026, 8:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7cbf0c014819088f80fccfc1d2341 completed March 28, 2026, 12:39 p.m.
Created at: March 27, 2026, 2:52 p.m.