Triple

T8309360
Position Surface form Disambiguated ID Type / Status
Subject Ana Villafañe E194549 entity
Predicate socialMediaPlatform P57 FINISHED
Object Twitter E3345 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: Twitter | Statement: [Ana Villafañe, socialMediaPlatform, Twitter]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Twitter
Context triple: [Ana Villafañe, socialMediaPlatform, Twitter]
  • A. Tweeter
    Tweeter is a fictional character from the Traveling Wilburys’ song “Tweeter and the Monkey Man,” depicted as a small-time criminal entangled in a noir-style tale of crime and betrayal.
  • B. Twitter, Inc. chosen
    Twitter, Inc. was a major social media and microblogging company best known for its real-time short-message platform that shaped online news, politics, and public discourse worldwide.
  • C. Weibo
    Weibo is a major Chinese microblogging and social media platform widely used for news, entertainment, and public discourse.
  • D. Instagram
    Instagram is a popular photo and video sharing social media platform known for its visual content, stories, and influencer culture.
  • E. Tweeter Center
    Tweeter Center was a former name of the large outdoor concert amphitheater now known as the Hollywood Casino Amphitheatre in Tinley Park, Illinois.
  • 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_69ca82e613e88190bf8139669bbd0d53 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb7f2d2c30819095075940479b75a7 completed March 31, 2026, 8 a.m.
NED1 Entity disambiguation (via context triple) batch_69cd95665390819089c8becad018cf51 completed April 1, 2026, 10 p.m.
Created at: March 30, 2026, 5:54 p.m.