Triple

T401463
Position Surface form Disambiguated ID Type / Status
Subject FASER E9291 entity
Predicate complements P162 FINISHED
Object CMS E3747 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: CMS | Statement: [FASER, complements, CMS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: CMS
Context triple: [FASER, complements, CMS]
  • A. CMS chosen
    CMS is a major general-purpose particle physics detector at CERN’s Large Hadron Collider, designed to investigate fundamental particles and forces, including the Higgs boson.
  • B. MIT CMS/W
    MIT CMS/W is an academic program at the Massachusetts Institute of Technology that focuses on the study and practice of media, communication, and writing across diverse platforms and disciplines.
  • C. WordPress
    WordPress is a widely used open-source content management system that enables users to create, manage, and publish websites and blogs through a user-friendly, web-based interface.
  • D. Ning
    Ning is an online platform that enables users and organizations to create their own custom social networks and communities.
  • E. SharePoint
    SharePoint is a Microsoft web-based collaboration and document management platform used for creating intranet sites, managing content, and enabling team collaboration within organizations.
  • 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_69a2e8004cb88190b92ed1add6abf41a completed Feb. 28, 2026, 1:05 p.m.
NER Named-entity recognition batch_69a2ec9f77888190bcc2bc68d201ed35 completed Feb. 28, 2026, 1:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69a410410c108190990d4d5ef2e7ff61 completed March 1, 2026, 10:09 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.