Triple

T3984060
Position Surface form Disambiguated ID Type / Status
Subject Phil S. Baran E86827 entity
Predicate familyName P18 FINISHED
Object Baran E187377 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: Baran | Statement: [Phil S. Baran, familyName, Baran]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Baran
Context triple: [Phil S. Baran, familyName, Baran]
  • A. Baran chosen
    Baran is a surname most notably associated with Paul Baran, a pioneering engineer of packet-switched networks and early internet technology.
  • B. Baran
    Baran is a city in the Hadoti region of Rajasthan, India, known for its historical temples, forts, and proximity to natural attractions like waterfalls and wildlife sanctuaries.
  • C. Barcha
    Barcha is the surname of Mercedes Barcha, the Colombian wife and lifelong companion of Nobel Prize–winning author Gabriel García Márquez.
  • D. Bara
    Bara is a town in Pakistan’s Khyber District, known as a key settlement in the Khyber Pass region with strategic and commercial significance.
  • E. Hazaragi
    Hazaragi is a variety of Persian primarily spoken by the Hazara people of central Afghanistan and surrounding regions, distinguished by its unique phonology and significant Turkic and Mongolic influences.
  • 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_69aed93fd9d4819085d3b2137d2346cb completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aef9de58d48190969f354a1bf0df94 completed March 9, 2026, 4:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69b540284d548190821d37b68974a2d2 completed March 14, 2026, 11:02 a.m.
Created at: March 9, 2026, 3:33 p.m.