Triple

T10780689
Position Surface form Disambiguated ID Type / Status
Subject Thivim railway station E254310 entity
Predicate stationCode P1289 FINISHED
Object THVM
THVM is the station code for Thivim railway station, a key rail stop in North Goa, India, serving as an access point to popular nearby beaches and towns.
 E886374 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: THVM | Statement: [Thivim railway station, stationCode, THVM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: THVM
Context triple: [Thivim railway station, stationCode, THVM]
  • A. TVH
    TVH is an Indian real estate and infrastructure development company known for sponsoring chess legend Viswanathan Anand.
  • B. THS
    THS is the stock ticker symbol for TreeHouse Foods, a U.S.-based manufacturer of private-label packaged foods and beverages.
  • C. GVM
    GVM is the vehicle registration code used for motor vehicles registered in the district of Nordwestmecklenburg in the German state of Mecklenburg-Vorpommern.
  • D. TAVHL
    TAVHL is the stock ticker symbol for TAV Airports Holding, a Turkish company that develops and operates airport terminals and related services.
  • E. TH
    TH is the two-letter ISO 3166-1 alpha-2 country code assigned to Thailand for international standardization and identification.
  • 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: THVM
Triple: [Thivim railway station, stationCode, THVM]
Generated description
THVM is the station code for Thivim railway station, a key rail stop in North Goa, India, serving as an access point to popular nearby beaches and towns.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: THVM
Target entity description: THVM is the station code for Thivim railway station, a key rail stop in North Goa, India, serving as an access point to popular nearby beaches and towns.
  • A. TVH
    TVH is an Indian real estate and infrastructure development company known for sponsoring chess legend Viswanathan Anand.
  • B. THS
    THS is the stock ticker symbol for TreeHouse Foods, a U.S.-based manufacturer of private-label packaged foods and beverages.
  • C. GVM
    GVM is the vehicle registration code used for motor vehicles registered in the district of Nordwestmecklenburg in the German state of Mecklenburg-Vorpommern.
  • D. TAVHL
    TAVHL is the stock ticker symbol for TAV Airports Holding, a Turkish company that develops and operates airport terminals and related services.
  • E. TH
    TH is the two-letter ISO 3166-1 alpha-2 country code assigned to Thailand for international standardization and identification.
  • 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_69d6aa609f008190a294200aefcb7bd5 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d732c48c488190a2b3162202b74726 completed April 9, 2026, 5:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69de55fbfc70819098eb40cf0d1b9e8c completed April 14, 2026, 2:58 p.m.
NEDg Description generation batch_69de5eacae148190b7ca2da87427572e completed April 14, 2026, 3:35 p.m.
NED2 Entity disambiguation (via description) batch_69de6397ff688190b6788489895a5360 completed April 14, 2026, 3:56 p.m.
Created at: April 8, 2026, 9:17 p.m.