Triple

T6978213
Position Surface form Disambiguated ID Type / Status
Subject Yantai E161767 entity
Predicate hasCountyLevelCity P27799 FINISHED
Object Penglai E448644 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: Penglai | Statement: [Yantai, hasCountyLevelCity, Penglai]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Penglai
Context triple: [Yantai, hasCountyLevelCity, Penglai]
  • A. Penglai chosen
    Penglai is a coastal city in northeastern Shandong, China, famed in Chinese mythology and history as a legendary isle of immortals and a scenic seaside destination.
  • B. Longkou
    Longkou is a coastal city in northeastern Shandong Province, China, known for its port, marine-based industries, and production of Longkou vermicelli.
  • C. Laizhou
    Laizhou is a county-level coastal city in northeastern Shandong Province, China, known for its salt, gold, and chemical industries.
  • D. Kaiping
    Kaiping is a county-level city in Guangdong Province, China, known for its distinctive diaolou watchtowers and as part of the Sze Yup region with a strong overseas Chinese heritage.
  • E. Caishikou
    Caishikou is a subway station in central Beijing that serves as an important stop on the city’s urban rail network.
  • 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_69c68854a0d88190bc0bf82263f1afce completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6db68d25c8190a1776908619ad979 completed March 27, 2026, 7:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69c761b614e88190877455edd5f64cf1 completed March 28, 2026, 5:05 a.m.
Created at: March 27, 2026, 2:31 p.m.