Triple

T2342767
Position Surface form Disambiguated ID Type / Status
Subject East Midlands Airport E45062 entity
Predicate IATAcode P418 FINISHED
Object EMA
EMA is the three-letter IATA airport code for East Midlands Airport in England, which serves the East Midlands region with domestic and international flights.
 E258891 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: EMA | Statement: [East Midlands Airport, IATAcode, EMA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: EMA
Context triple: [East Midlands Airport, IATAcode, EMA]
  • A. EMA
    EMA is the European Union’s regulatory authority responsible for the scientific evaluation, supervision, and safety monitoring of medicines.
  • B. Em
    Em is a common shortened form of the given name Emma, often used as an informal nickname.
  • C. ERM
    ERM is the French-language abbreviation for Belgium’s Royal Military Academy, the country’s principal institution for training future officers of the armed forces.
  • D. ERM
    ERM is a European Union system designed to reduce exchange rate variability and achieve monetary stability in preparation for economic and monetary union.
  • E. ENA
    ENA is a prestigious French grande école that trained many of the country’s top civil servants and political leaders.
  • 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: EMA
Triple: [East Midlands Airport, IATAcode, EMA]
Generated description
EMA is the three-letter IATA airport code for East Midlands Airport in England, which serves the East Midlands region with domestic and international flights.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: EMA
Target entity description: EMA is the three-letter IATA airport code for East Midlands Airport in England, which serves the East Midlands region with domestic and international flights.
  • A. EMA
    EMA is the European Union’s regulatory authority responsible for the scientific evaluation, supervision, and safety monitoring of medicines.
  • B. Em
    Em is a common shortened form of the given name Emma, often used as an informal nickname.
  • C. ERM
    ERM is the French-language abbreviation for Belgium’s Royal Military Academy, the country’s principal institution for training future officers of the armed forces.
  • D. ERM
    ERM is a European Union system designed to reduce exchange rate variability and achieve monetary stability in preparation for economic and monetary union.
  • E. ENA
    ENA is a prestigious French grande école that trained many of the country’s top civil servants and political leaders.
  • 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_69a88917935081909b755dbf38e81024 completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abc6ae33e881909a81a0c0def59059 completed March 7, 2026, 6:33 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae9622cdb08190835222482bd22cf4 completed March 9, 2026, 9:42 a.m.
NEDg Description generation batch_69ae977776ec8190ad5f7ce4594d73d9 completed March 9, 2026, 9:48 a.m.
NED2 Entity disambiguation (via description) batch_69ae9b64daf08190afa6898242bde864 completed March 9, 2026, 10:05 a.m.
Created at: March 4, 2026, 7:52 p.m.