Triple

T7462630
Position Surface form Disambiguated ID Type / Status
Subject Modo Hockey E176287 entity
Predicate nickname P55 FINISHED
Object Modo E666350 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: Modo | Statement: [Modo Hockey, nickname, Modo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Modo
Context triple: [Modo Hockey, nickname, Modo]
  • A. Modo chosen
    Modo is a Swedish professional ice hockey club known for developing numerous NHL players and competing in the country’s top leagues.
  • B. Modo AIK
    Modo AIK was the earlier name of the Swedish professional ice hockey club now known as Modo Hockey, based in Örnsköldsvik.
  • C. Autodesk 3ds Max
    Autodesk 3ds Max is a professional 3D modeling, animation, and rendering software widely used in game development, film, and architectural visualization.
  • D. Autodesk Maya
    Autodesk Maya is a professional 3D computer graphics application widely used in film, television, and game development for modeling, animation, simulation, and rendering.
  • E. LightWave 3D
    LightWave 3D is a professional 3D modeling, rendering, and animation software package widely used in film, television, and visual effects production.
  • 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_69c69f21632481908bf83f6c6da897e3 completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f3d80ae08190ba383066cf0cb2ce completed March 27, 2026, 9:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69c83c5bab90819093431470e0e8c0e3 completed March 28, 2026, 8:38 p.m.
Created at: March 27, 2026, 3:39 p.m.