Triple

T20353738
Position Surface form Disambiguated ID Type / Status
Subject Umaria railway station E496086 entity
Predicate hasStationCode P1289 FINISHED
Object UMR
UMR is the station code for Umaria railway station in Madhya Pradesh, India, used for train scheduling, ticketing, and railway operations.
 E1425145 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: UMR | Statement: [Umaria railway station, hasStationCode, UMR]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: UMR
Context triple: [Umaria railway station, hasStationCode, UMR]
  • A. URM
    URM is the commonly used abbreviation for Union Rescue Mission, a prominent Christian homeless services organization based in Los Angeles.
  • B. URM
    URM is the commonly used Lithuanian abbreviation for the country’s Ministry of Foreign Affairs.
  • C. URM
    URM is the National Rail station code for Urmston railway station in Greater Manchester, England.
  • D. UM
    UM is the commonly used abbreviation for the University of Miami, a private research university located in Coral Gables, Florida.
  • E. UM
    UM is the regional vehicle registration code used for the district of Uckermark in the German state of Brandenburg.
  • 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: UMR
Triple: [Umaria railway station, hasStationCode, UMR]
Generated description
UMR is the station code for Umaria railway station in Madhya Pradesh, India, used for train scheduling, ticketing, and railway operations.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: UMR
Target entity description: UMR is the station code for Umaria railway station in Madhya Pradesh, India, used for train scheduling, ticketing, and railway operations.
  • A. URM
    URM is the commonly used Lithuanian abbreviation for the country’s Ministry of Foreign Affairs.
  • B. URM
    URM is the commonly used abbreviation for Union Rescue Mission, a prominent Christian homeless services organization based in Los Angeles.
  • C. URM
    URM is the National Rail station code for Urmston railway station in Greater Manchester, England.
  • D. UM
    UM is the commonly used abbreviation for Maastricht University, a public research university located in Maastricht, the Netherlands.
  • E. UM
    UM is the stock ticker symbol for MRU, the Canadian food and pharmacy retail company Metro Inc.
  • 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_69e0b4a3f7f48190b37f354574028ca6 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e67851c7088190ba960a33c6dfa824 completed April 20, 2026, 7:02 p.m.
NED1 Entity disambiguation (via context triple) batch_6a086967aef88190b138be722f410e82 completed May 16, 2026, 12:56 p.m.
NEDg Description generation batch_6a0869f7b4208190858fdeae882e008d completed May 16, 2026, 12:58 p.m.
NED2 Entity disambiguation (via description) batch_6a086a893f1c81909bc40e3c4db0e147 completed May 16, 2026, 1 p.m.
Created at: April 16, 2026, 11:25 a.m.