Triple

T17071979
Position Surface form Disambiguated ID Type / Status
Subject Lawrence Mark E414243 entity
Predicate notableWork P4 FINISHED
Object Vivo E805089 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: Vivo | Statement: [Lawrence Mark, notableWork, Vivo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vivo
Context triple: [Lawrence Mark, notableWork, Vivo]
  • A. Vivo
    Vivo is a Chinese smartphone manufacturer that has served as a major title sponsor of the Indian Premier League (IPL), boosting its brand visibility across India.
  • B. Vivo chosen
    Vivo is an animated musical film produced by Sony Pictures Animation that follows a music-loving kinkajou on a heartfelt adventure to deliver a song.
  • C. Xiaomi
    Xiaomi is a major Chinese electronics and smartphone manufacturer known for its affordable, feature-rich devices and rapidly growing global presence.
  • D. Huawei Nova
    Huawei Nova is a mid-range Android smartphone from Huawei’s Nova lineup, known for its stylish design and solid camera performance.
  • E. Visso
    Visso is a historic village in the Umbria region of central Italy, known for its medieval architecture and location within the scenic Valnerina valley.
  • 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_69e3dbc1b7d48190979a848b4188cb22 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.