Triple

T14720791
Position Surface form Disambiguated ID Type / Status
Subject Mount Hong E345806 entity
Predicate gaveNameTo P744 FINISHED
Object Hongshan District E69519 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: Hongshan District | Statement: [Mount Hong, gaveNameTo, Hongshan District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hongshan District
Context triple: [Mount Hong, gaveNameTo, Hongshan District]
  • A. Hongshan District
    Hongshan District is the central urban district and administrative seat of the prefecture-level city of Chifeng in Inner Mongolia, China.
  • B. Hongshan District chosen
    Hongshan District is an urban district of Wuhan in Hubei Province, China, known for its educational institutions, technology parks, and major transportation hubs.
  • C. Honggu District
    Honggu District is an administrative urban district of Lanzhou in Gansu Province, China, known for its role in the city's industrial and resource-based development.
  • D. Tieshan District
    Tieshan District is an urban administrative district of the prefecture-level city of Huangshi in Hubei Province, China, known for its industrial and mining activities.
  • E. Zhanqian District
    Zhanqian District is an urban administrative district under the jurisdiction of Yingkou City in Liaoning Province, China.
  • 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_69d822e5911c8190ba589f957dbd9ba7 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69dec25d56fc8190871873ca55d49272 completed April 14, 2026, 10:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffdbb7b1e48190b55c40e0cb837446 completed May 10, 2026, 1:13 a.m.
Created at: April 10, 2026, 1:29 a.m.