Triple

T1407237
Position Surface form Disambiguated ID Type / Status
Subject Sanaa Lathan E31721 entity
Predicate givenName P17 FINISHED
Object Sanaa E99845 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: Sanaa | Statement: [Sanaa Lathan, givenName, Sanaa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sanaa
Context triple: [Sanaa Lathan, givenName, Sanaa]
  • A. Sanaa chosen
    Sanaa is a table-service restaurant at Disney’s Animal Kingdom Lodge known for its African-inspired cuisine with Indian flavors and savanna views of roaming wildlife.
  • B. Sanaʽa
    Sanaʽa is the historic capital and one of the largest cities of Yemen, renowned for its ancient architecture and cultural significance in the Arabian Peninsula.
  • C. SANAA
    SANAA is a renowned Japanese architectural firm, led by Kazuyo Sejima and Ryue Nishizawa, celebrated for its minimalist, light-filled designs and influential contemporary projects worldwide.
  • D. Taiz
    Taiz is one of Yemen’s largest and historically most important cities, known as a cultural and intellectual center in the country.
  • E. Salalah
    Salalah is a coastal city in southern Oman known for its monsoon-cooled climate, lush green landscapes, and role as a regional tourism and commercial hub.
  • 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_69a49918e1f88190ba610f9dc8114578 completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c3be10348190ade8a73780d2c008 completed March 1, 2026, 10:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69ace5770ea08190ac91b47a4ed5bf35 completed March 8, 2026, 2:56 a.m.
Created at: March 1, 2026, 7:59 p.m.