Triple

T12500484
Position Surface form Disambiguated ID Type / Status
Subject Sala Daeng Station E298805 entity
Predicate hasCode P9567 FINISHED
Object S2
S2 is the station code for Sala Daeng Station on Bangkok's BTS Skytrain system.
 E988641 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: S2 | Statement: [Sala Daeng Station, hasCode, S2]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: S2
Context triple: [Sala Daeng Station, hasCode, S2]
  • A. S2
    S2 is a line of the Munich S-Bahn rapid transit network that runs through the central trunk route and serves suburban areas around Munich.
  • B. S2
    S2 is one of the commuter rail lines of the Nuremberg S-Bahn network in Germany, serving suburban and regional routes around the city.
  • C. S2
    S2 is a commuter rail line of the Rhine-Main S-Bahn network serving the Frankfurt metropolitan region in Germany.
  • D. S2
    S2 is a line of Berlin's S-Bahn rapid transit network that connects northern and southern suburbs through the city center.
  • E. S2
    S2 is a commuter rail line of the Stuttgart S-Bahn network serving the Stuttgart metropolitan area in Germany.
  • 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: S2
Triple: [Sala Daeng Station, hasCode, S2]
Generated description
S2 is the station code for Sala Daeng Station on Bangkok's BTS Skytrain system.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: S2
Target entity description: S2 is the station code for Sala Daeng Station on Bangkok's BTS Skytrain system.
  • A. S2
    S2 is a line of the Munich S-Bahn rapid transit network that runs through the central trunk route and serves suburban areas around Munich.
  • B. S2
    S2 is a line of Berlin's S-Bahn rapid transit network that connects northern and southern suburbs through the city center.
  • C. S2
    S2 is a commuter rail line of the Stuttgart S-Bahn network serving the Stuttgart metropolitan area in Germany.
  • D. S2
    S2 is a commuter rail line of the Rhine-Main S-Bahn network serving the Frankfurt metropolitan region in Germany.
  • E. S2
    S2 is a commuter rail line within Germany’s Rhine-Ruhr S-Bahn network, connecting various cities and suburbs in the densely populated Rhine-Ruhr metropolitan region.
  • 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_69d6ada4cd388190ae3bbf83ff87057a completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94dfbb2a48190a231b02cfa990565 completed April 10, 2026, 7:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69f65570401c819084f9db2eff5fdf3e completed May 2, 2026, 7:50 p.m.
NEDg Description generation batch_69f6566dccc0819085e059c7b0288f6c completed May 2, 2026, 7:54 p.m.
NED2 Entity disambiguation (via description) batch_69f657aec8fc8190b3b08ccb95595958 completed May 2, 2026, 7:59 p.m.
Created at: April 8, 2026, 9:57 p.m.