Triple

T1635539
Position Surface form Disambiguated ID Type / Status
Subject ThinkVision E35352 entity
Predicate manufacturer P490 FINISHED
Object Lenovo E72301 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: Lenovo | Statement: [ThinkVision, manufacturer, Lenovo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lenovo
Context triple: [ThinkVision, manufacturer, Lenovo]
  • A. Lenovo chosen
    Lenovo is a multinational technology company best known for manufacturing and selling personal computers, laptops, smartphones, and other consumer electronics worldwide.
  • B. Acer
    Acer is a Taiwanese multinational hardware and electronics corporation best known for manufacturing laptops, desktops, monitors, and other computer-related products.
  • C. Dell
    Dell is a major American technology company best known for designing, manufacturing, and selling personal computers, servers, and related IT products and services worldwide.
  • D. Compaq
    Compaq was a major American computer company best known for its popular line of personal computers and for being one of the largest PC manufacturers before its acquisition by Hewlett-Packard.
  • E. Toshiba
    Toshiba is a major Japanese multinational conglomerate known for its electronics, semiconductors, and information technology products and services.
  • 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_69a886036bc081909ff5de16dbe5e8ea completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a90a17e8e08190afb78a953ab920ec completed March 5, 2026, 4:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad6099979481908e2c506323d546dd completed March 8, 2026, 11:42 a.m.
Created at: March 4, 2026, 7:28 p.m.