Triple

T17626594
Position Surface form Disambiguated ID Type / Status
Subject Jundu Mountains E429859 entity
Predicate near P350 FINISHED
Object Huairou District NE NERFINISHED

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: Huairou District | Statement: [Jundu Mountains, near, Huairou District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Huairou District
Context triple: [Jundu Mountains, near, Huairou District]
  • A. Huairou District chosen
    Huairou District is a suburban district in northern Beijing known for its scenic mountains, sections of the Great Wall, and popular tourist attractions such as Mutianyu.
  • B. Ōmiya-ku
    Ōmiya-ku is a central ward of Saitama City in Japan, known as a major commercial and transportation hub in the Greater Tokyo area.
  • C. Higashi-Nippori district
    Higashi-Nippori is a residential and commercial neighborhood in Tokyo known for its traditional shitamachi atmosphere and proximity to major transport hubs like Nippori Station.
  • D. Kōtō ward
    Kōtō ward is a special ward in eastern Tokyo known for its mix of traditional shitamachi neighborhoods, waterfront areas, and modern residential and commercial districts.
  • E. Aoyama district
    Aoyama district is an upscale neighborhood in central Tokyo known for its fashionable boutiques, trendy cafes, art galleries, and modern architecture.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d889e37f308190a6aa0a69daff86c7 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e46dbd122c8190a5db8c0088c81034 completed April 19, 2026, 5:53 a.m.
Created at: April 10, 2026, 5:52 a.m.