Triple

T1299719
Position Surface form Disambiguated ID Type / Status
Subject Jan Mayen E27733 entity
Predicate hasICAOCode P419 FINISHED
Object ENJA
ENJA is the ICAO airport code for Jan Mayensfield, the airfield serving the remote Norwegian island of Jan Mayen in the Arctic Ocean.
 E147616 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: ENJA | Statement: [Jan Mayen, hasICAOCode, ENJA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ENJA
Context triple: [Jan Mayen, hasICAOCode, ENJA]
  • A. Enz
    The Enz is a river in southwestern Germany that flows through the Black Forest region before joining the Neckar River.
  • B. Enga
    Enga is the popular nickname of Vålerenga Fotball, a prominent Oslo-based Norwegian football club known for its passionate supporters.
  • C. Oshiwambo
    Oshiwambo is a Bantu language (or cluster of closely related dialects) widely spoken by the Ovambo people in northern Namibia and southern Angola.
  • D. Handai
    Handai is the common Japanese abbreviation for Osaka University, a leading national research university based in Osaka, Japan.
  • E. ENBA
    ENBA is the commonly used abbreviation for the Escola Nacional de Belas Artes, a prominent national fine arts school.
  • 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: ENJA
Triple: [Jan Mayen, hasICAOCode, ENJA]
Generated description
ENJA is the ICAO airport code for Jan Mayensfield, the airfield serving the remote Norwegian island of Jan Mayen in the Arctic Ocean.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ENJA
Target entity description: ENJA is the ICAO airport code for Jan Mayensfield, the airfield serving the remote Norwegian island of Jan Mayen in the Arctic Ocean.
  • A. Enz
    The Enz is a river in southwestern Germany that flows through the Black Forest region before joining the Neckar River.
  • B. Enga
    Enga is the popular nickname of Vålerenga Fotball, a prominent Oslo-based Norwegian football club known for its passionate supporters.
  • C. Oshiwambo
    Oshiwambo is a Bantu language (or cluster of closely related dialects) widely spoken by the Ovambo people in northern Namibia and southern Angola.
  • D. Handai
    Handai is the common Japanese abbreviation for Osaka University, a leading national research university based in Osaka, Japan.
  • E. ENBA
    ENBA is the commonly used abbreviation for the Escola Nacional de Belas Artes, a prominent national fine arts school.
  • 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_69a496d6682881909ba658f1c1e0e2b0 completed March 1, 2026, 7:43 p.m.
NER Named-entity recognition batch_69a4c11314a48190ab4efb8b1acdce50 completed March 1, 2026, 10:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69acacc631b88190853948eeb5f24527 completed March 7, 2026, 10:55 p.m.
NEDg Description generation batch_69acad4b2234819097d94df7812d3b13 completed March 7, 2026, 10:57 p.m.
NED2 Entity disambiguation (via description) batch_69acadf94f0881908b0be66e37b8c04a completed March 7, 2026, 11 p.m.
Created at: March 1, 2026, 7:51 p.m.