Triple

T11702642
Position Surface form Disambiguated ID Type / Status
Subject Erechim E278162 entity
Predicate airportIATAcode P418 FINISHED
Object ERM
ERM is the IATA airport code for Erechim Airport, which serves the city of Erechim in Brazil.
 E941732 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: ERM | Statement: [Erechim, airportIATAcode, ERM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ERM
Context triple: [Erechim, airportIATAcode, ERM]
  • A. 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.
  • B. ERM
    ERM is a European Union system designed to reduce exchange rate variability and achieve monetary stability in preparation for economic and monetary union.
  • C. ER
    ER is the IATA airline designator assigned to SereneAir, a Pakistani low-cost carrier.
  • D. ER
    ER is the vehicle registration code assigned to the German city of Erlangen in the state of Bavaria.
  • E. ER
    ER is the zone code for Eastern Railway, one of the major railway zones of Indian Railways headquartered in Kolkata.
  • 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: ERM
Triple: [Erechim, airportIATAcode, ERM]
Generated description
ERM is the IATA airport code for Erechim Airport, which serves the city of Erechim in Brazil.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ERM
Target entity description: ERM is the IATA airport code for Erechim Airport, which serves the city of Erechim in Brazil.
  • A. 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.
  • B. ERM
    ERM is a European Union system designed to reduce exchange rate variability and achieve monetary stability in preparation for economic and monetary union.
  • C. ER
    ER is the vehicle registration code assigned to the German city of Erlangen in the state of Bavaria.
  • D. ER
    ER is the zone code for Eastern Railway, one of the major railway zones of Indian Railways headquartered in Kolkata.
  • E. ER
    ER is the abbreviation used to designate the Eastern Region of British Rail, a major administrative division of the former British railway network covering eastern England.
  • 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_69d6aafe02d881909900d54ad7d4af84 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a49b1080819096593733ee48a187 completed April 10, 2026, 7:19 a.m.
NED1 Entity disambiguation (via context triple) batch_69ef83525ae081909ee6f3fbb5d37dd7 completed April 27, 2026, 3:40 p.m.
NEDg Description generation batch_69ef9b673120819097b542bb9a8f8bdb completed April 27, 2026, 5:22 p.m.
NED2 Entity disambiguation (via description) batch_69efd683366881909dd9621e7c57d0be completed April 27, 2026, 9:34 p.m.
Created at: April 8, 2026, 9:40 p.m.