Triple

T12596147
Position Surface form Disambiguated ID Type / Status
Subject MESA E300735 entity
Predicate acronym P43 FINISHED
Object MESA E300735 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: MESA | Statement: [MESA, acronym, MESA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: MESA
Context triple: [MESA, acronym, MESA]
  • A. MESA chosen
    MESA is a large, long-term medical research study that investigates the prevalence, risk factors, and progression of cardiovascular disease in a diverse, multi-ethnic population.
  • B. Mesa
    Mesa is a pioneering systems programming language developed at Xerox PARC in the 1970s, notable for its strong typing, modularity, and influence on later languages and operating system design.
  • C. Mesa, Arizona
    Mesa, Arizona is a large city in the Phoenix metropolitan area known for its desert climate, suburban communities, and role as a major spring training hub for Major League Baseball.
  • D. Santa Mesa
    Santa Mesa is a historic district in Manila, Philippines, known for its role as a key battleground during the early stages of the Philippine–American War.
  • E. Modasa
    Modasa is a town in the Indian state of Gujarat that serves as the administrative headquarters of Aravalli district.
  • 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_69d7bdea2ca881908f379526c13b1145 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d954cf33b88190bff339fcd3142cc8 completed April 10, 2026, 7:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69f65ec49af881908abb948567b82b74 completed May 2, 2026, 8:29 p.m.
Created at: April 9, 2026, 5:08 p.m.