Triple

T5558912
Position Surface form Disambiguated ID Type / Status
Subject Steve Meretzky E145716 entity
Predicate employer P7 FINISHED
Object Infocom E144627 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: Infocom | Statement: [Steve Meretzky, employer, Infocom]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Infocom
Context triple: [Steve Meretzky, employer, Infocom]
  • A. Infocom chosen
    Infocom was a pioneering American software company best known for its influential text adventure games in the 1980s.
  • B. Telcordia Technologies
    Telcordia Technologies is a telecommunications research and development company known for creating industry standards and software solutions for network planning, management, and operations.
  • C. BBN Technologies
    BBN Technologies is an American research and development company renowned for its pioneering work in computer networking and its key role in creating the ARPANET, the precursor to the modern internet.
  • D. Merit Network
    Merit Network is a nonprofit regional research and education network in Michigan that played a key role in operating and developing early national internet infrastructure.
  • E. Lucent Technologies
    Lucent Technologies was a major American telecommunications equipment company, spun off from AT&T, known for its Bell Labs research arm and contributions to networking and communications technology.
  • 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_69c008fcaf788190bafa02a1917ee73b completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c0201529a88190bf0135e032b048ea completed March 22, 2026, 5 p.m.
NED1 Entity disambiguation (via context triple) batch_69c028424ddc8190869b530a8eb43f50 completed March 22, 2026, 5:34 p.m.
Created at: March 22, 2026, 3:36 p.m.