Triple

T5914529
Position Surface form Disambiguated ID Type / Status
Subject Sultan Hasanuddin International Airport E131544 entity
Predicate IATAcode P418 FINISHED
Object UPG
UPG is the IATA airport code for Sultan Hasanuddin International Airport serving Makassar in South Sulawesi, Indonesia.
 E555088 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: UPG | Statement: [Sultan Hasanuddin International Airport, IATAcode, UPG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: UPG
Context triple: [Sultan Hasanuddin International Airport, IATAcode, UPG]
  • A. UPP
    UPP is a reporting mark used by the Union Pacific Railroad to identify certain passenger cars and related rolling stock in its fleet.
  • B. UPY
    UPY is a reporting mark used by Union Pacific Railroad, primarily identifying its yard and switching locomotives.
  • C. UP
    UP is the standard reporting mark used to identify rail equipment owned or operated by the Union Pacific Railroad in North America.
  • D. UP
    UP is a leading South African public research university located in Pretoria, known for its comprehensive range of academic programs and strong research output.
  • E. UP
    UP is the Indian state of Uttar Pradesh, the country’s most populous state and a major political and cultural center in northern India.
  • 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: UPG
Triple: [Sultan Hasanuddin International Airport, IATAcode, UPG]
Generated description
UPG is the IATA airport code for Sultan Hasanuddin International Airport serving Makassar in South Sulawesi, Indonesia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: UPG
Target entity description: UPG is the IATA airport code for Sultan Hasanuddin International Airport serving Makassar in South Sulawesi, Indonesia.
  • A. UPP
    UPP is a reporting mark used by the Union Pacific Railroad to identify certain passenger cars and related rolling stock in its fleet.
  • B. UPY
    UPY is a reporting mark used by Union Pacific Railroad, primarily identifying its yard and switching locomotives.
  • C. UP
    UP is the standard reporting mark used to identify rail equipment owned or operated by the Union Pacific Railroad in North America.
  • D. UP
    UP is the Indian state of Uttar Pradesh, the country’s most populous state and a major political and cultural center in northern India.
  • E. UP
    UP is a leading South African public research university located in Pretoria, known for its comprehensive range of academic programs and strong research output.
  • 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_69c008593a44819081a07ae0efe6c574 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c037b9f0908190ad854e5f2600f114 completed March 22, 2026, 6:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0c02430bc8190a63b91b6dbdbc9f2 completed March 23, 2026, 4:23 a.m.
NEDg Description generation batch_69c0c0cb4fac8190b1f73d6707200112 completed March 23, 2026, 4:25 a.m.
NED2 Entity disambiguation (via description) batch_69c0c1f6fe60819080a00976740b6a9c completed March 23, 2026, 4:30 a.m.
Created at: March 22, 2026, 3:59 p.m.