Triple

T18836216
Position Surface form Disambiguated ID Type / Status
Subject Morena railway station E460671 entity
Predicate stationCode P1289 FINISHED
Object MRA
MRA is the station code for Morena railway station, a rail transport facility serving the city of Morena in the Indian state of Madhya Pradesh.
 E1345517 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: MRA | Statement: [Morena railway station, stationCode, MRA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MRA
Context triple: [Morena railway station, stationCode, MRA]
  • A. MRA
    MRA is the IATA airport code for Misrata International Airport, a commercial airport serving the city of Misrata in Libya.
  • B. MRIA
    MRIA is the post-nominal title indicating membership of the Royal Irish Academy, an all-Ireland body of distinguished scholars and scientists.
  • C. MRIA
    MRIA is the abbreviation for Mattala Rajapaksa International Airport, a major international airport located in southern Sri Lanka.
  • D. MRI
    MRI (Matz's Ruby Interpreter) is the standard reference implementation of the Ruby programming language, written in C and known for prioritizing simplicity and developer happiness.
  • E. MRIT
    MRIT is an abbreviation for the Matsushita Research Institute Tokyo, a Japanese research organization historically associated with Panasonic’s advanced technology and electronics R&D.
  • 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: MRA
Triple: [Morena railway station, stationCode, MRA]
Generated description
MRA is the station code for Morena railway station, a rail transport facility serving the city of Morena in the Indian state of Madhya Pradesh.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MRA
Target entity description: MRA is the station code for Morena railway station, a rail transport facility serving the city of Morena in the Indian state of Madhya Pradesh.
  • A. MRA
    MRA is the IATA airport code for Misrata International Airport, a commercial airport serving the city of Misrata in Libya.
  • B. MRIA
    MRIA is the post-nominal title indicating membership of the Royal Irish Academy, an all-Ireland body of distinguished scholars and scientists.
  • C. MRIA
    MRIA is the abbreviation for Mattala Rajapaksa International Airport, a major international airport located in southern Sri Lanka.
  • D. MRI
    MRI (Matz's Ruby Interpreter) is the standard reference implementation of the Ruby programming language, written in C and known for prioritizing simplicity and developer happiness.
  • E. MRIT
    MRIT is an abbreviation for the Matsushita Research Institute Tokyo, a Japanese research organization historically associated with Panasonic’s advanced technology and electronics R&D.
  • 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_69d8dcfa11e4819090ab1ef5bdcd2b2e completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5a99d491c81909d8e55ac45621d44 completed April 20, 2026, 4:20 a.m.
NED1 Entity disambiguation (via context triple) batch_6a05676b320481909bea3533d2c7fc27 completed May 14, 2026, 6:10 a.m.
NEDg Description generation batch_6a0568910c20819093ab2c08b79698cf completed May 14, 2026, 6:15 a.m.
NED2 Entity disambiguation (via description) batch_6a056959a154819095fec2ee55e4734e completed May 14, 2026, 6:19 a.m.
Created at: April 10, 2026, 11:56 a.m.