Triple

T4217263
Position Surface form Disambiguated ID Type / Status
Subject Juyongguan E94248 entity
Predicate near P350 FINISHED
Object Badaling E73033 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: Badaling | Statement: [Juyongguan, near, Badaling]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Badaling
Context triple: [Juyongguan, near, Badaling]
  • A. Badaling chosen
    Badaling is the most visited and best-preserved section of the Great Wall of China, located in the mountains north of Beijing.
  • B. Jinji Fort
    Jinji Fort is a historic hilltop stronghold in Tamil Nadu, India, renowned as the Maratha king Rajaram I’s refuge and seat of resistance against Mughal forces in the late 17th century.
  • C. Badaling railway station
    Badaling railway station is a transport hub in Beijing that serves visitors traveling by train to the popular Badaling section of the Great Wall of China.
  • D. Tianmen
    Tianmen is a county-level city in central China's Hubei province, known for its location on the fertile Jianghan Plain and its role as a regional agricultural and transport hub.
  • E. Taj
    Taj was the former name of Esteghlal F.C., one of Iran’s most successful and popular football clubs.
  • 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_69b3451997e08190851db4a9a588837d completed March 12, 2026, 10:58 p.m.
NER Named-entity recognition batch_69b34beb470481909ceff19195417f19 completed March 12, 2026, 11:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5a855cfc08190acceced9cb80f41a completed March 14, 2026, 6:26 p.m.
Created at: March 12, 2026, 11:04 p.m.