Triple

T2899391
Position Surface form Disambiguated ID Type / Status
Subject Burma Railway E62618 entity
Predicate connects P390 FINISHED
Object Ban Pong
Ban Pong is a town in western Thailand that served as a key rail junction and starting point for the World War II–era Burma Railway.
 E307837 NE FINISHED

How this triple was built (4 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: Ban Pong | Statement: [Burma Railway, connects, Ban Pong]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ban Pong
Context triple: [Burma Railway, connects, Ban Pong]
  • A. Poh Pitu
    Poh Pitu was an early capital city of the Medang Kingdom, an ancient Javanese Hindu-Buddhist polity in what is now Indonesia.
  • B. Pang
    Pang is a variant transliteration of the Chinese surname commonly romanized as Peng.
  • C. Sam Poo Kong
    Sam Poo Kong is a historic Chinese temple complex in Semarang, Indonesia, revered as a cultural and religious site linked to the legendary admiral Zheng He.
  • D. Banna
    Banna is the Latin name of Birdoswald Roman Fort, a key military site along Hadrian’s Wall in Roman Britain.
  • E. Nam Ou
    Nam Ou is a significant river in northern Laos known for its scenic valleys, hydropower dams, and role in regional transport and livelihoods.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Ban Pong
Triple: [Burma Railway, connects, Ban Pong]
Generated description
Ban Pong is a town in western Thailand that served as a key rail junction and starting point for the World War II–era Burma Railway.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ban Pong
Target entity description: Ban Pong is a town in western Thailand that served as a key rail junction and starting point for the World War II–era Burma Railway.
  • A. Poh Pitu
    Poh Pitu was an early capital city of the Medang Kingdom, an ancient Javanese Hindu-Buddhist polity in what is now Indonesia.
  • B. Pang
    Pang is a variant transliteration of the Chinese surname commonly romanized as Peng.
  • C. Sam Poo Kong
    Sam Poo Kong is a historic Chinese temple complex in Semarang, Indonesia, revered as a cultural and religious site linked to the legendary admiral Zheng He.
  • D. Banna
    Banna is the Latin name of Birdoswald Roman Fort, a key military site along Hadrian’s Wall in Roman Britain.
  • E. Nam Ou
    Nam Ou is a significant river in northern Laos known for its scenic valleys, hydropower dams, and role in regional transport and livelihoods.
  • F. None of above. chosen

Provenance (5 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_69ab4c3e070c8190b78d3d2c005876dd completed March 6, 2026, 9:50 p.m.
NER Named-entity recognition batch_69abe0ad7bbc8190822738baa6935b74 completed March 7, 2026, 8:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69b0318c544881909f6aabfb2d25e724 completed March 10, 2026, 2:58 p.m.
NEDg Description generation batch_69b038fdc7e881909dd0b6fd4f692e4a completed March 10, 2026, 3:30 p.m.
NED2 Entity disambiguation (via description) batch_69b03bca591c81908b3734cc8f2e712b completed March 10, 2026, 3:42 p.m.
Created at: March 6, 2026, 10:10 p.m.