Triple

T3847795
Position Surface form Disambiguated ID Type / Status
Subject ZBAD E85214 entity
Predicate locatedIn P40 FINISHED
Object Daxing District E89779 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: Daxing District | Statement: [ZBAD, locatedIn, Daxing District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Daxing District
Context triple: [ZBAD, locatedIn, Daxing District]
  • A. Daxing District chosen
    Daxing District is a rapidly developing suburban district in southern Beijing, China, known for hosting the major Beijing Daxing International Airport and large-scale urban expansion.
  • B. Changping District
    Changping District is a suburban district in the northern part of Beijing, China, known for its historical sites and scenic mountainous landscapes.
  • C. Fangshan District
    Fangshan District is a suburban district in the southwest of Beijing, China, known for its mix of residential areas, industrial zones, and historical and natural attractions.
  • D. Haidian District
    Haidian District is a major urban district in northwest Beijing known for its universities, technology hubs, and historic imperial gardens.
  • E. Fengtai District
    Fengtai District is an urban district in southwestern Beijing, China, known for its mix of residential, industrial, and historical areas, including the site of the Marco Polo Bridge.
  • 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_69aed936de1c81908f91bed80f70abb2 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeebcc8a0481909c35161336bdfbf9 completed March 9, 2026, 3:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69beb0b17e288190b6014ad9c31a3b9f completed March 21, 2026, 2:52 p.m.
Created at: March 9, 2026, 3:18 p.m.