Triple

T8739575
Position Surface form Disambiguated ID Type / Status
Subject Vietnam Academy of Social Sciences E207467 entity
Predicate abbreviation P43 FINISHED
Object VASS
VASS is Vietnam’s leading national research institution dedicated to the study and development of the social sciences.
 E755785 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: VASS | Statement: [Vietnam Academy of Social Sciences, abbreviation, VASS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: VASS
Context triple: [Vietnam Academy of Social Sciences, abbreviation, VASS]
  • A. VASU
    VASU is the ICAO airport code for Surat Airport, a domestic airport serving the city of Surat in the Indian state of Gujarat.
  • B. VAB
    VAB is the commonly used abbreviation for NASA’s Vehicle Assembly Building, the massive structure at Kennedy Space Center where rockets are assembled before launch.
  • C. VABO
    VABO is the ICAO airport code assigned to Vadodara Airport in Gujarat, India.
  • D. VAN
    VAN is the standard abbreviation used for the Vancouver Canadians, a Minor League Baseball team based in Vancouver, British Columbia.
  • E. VOSA
    VOSA was an executive agency of the UK government responsible for enforcing vehicle safety and environmental standards, and regulating operators of heavy goods and public service vehicles.
  • 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: VASS
Triple: [Vietnam Academy of Social Sciences, abbreviation, VASS]
Generated description
VASS is Vietnam’s leading national research institution dedicated to the study and development of the social sciences.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: VASS
Target entity description: VASS is Vietnam’s leading national research institution dedicated to the study and development of the social sciences.
  • A. VASU
    VASU is the ICAO airport code for Surat Airport, a domestic airport serving the city of Surat in the Indian state of Gujarat.
  • B. VAB
    VAB is the commonly used abbreviation for NASA’s Vehicle Assembly Building, the massive structure at Kennedy Space Center where rockets are assembled before launch.
  • C. VABO
    VABO is the ICAO airport code assigned to Vadodara Airport in Gujarat, India.
  • D. VAN
    VAN is the standard abbreviation used for the Vancouver Canadians, a Minor League Baseball team based in Vancouver, British Columbia.
  • E. VOSA
    VOSA was an executive agency of the UK government responsible for enforcing vehicle safety and environmental standards, and regulating operators of heavy goods and public service vehicles.
  • 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_69ca835a03a081909d4d4cd01a18c9fb completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5d486e34819094a6c6ec26c047cf completed March 31, 2026, 11:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf42e7176c819097e313ed8e8ceb06 completed April 3, 2026, 4:32 a.m.
NEDg Description generation batch_69cf43ead588819094089bea94c27207 completed April 3, 2026, 4:36 a.m.
NED2 Entity disambiguation (via description) batch_69cf453fa3e4819082466c59649c2f35 completed April 3, 2026, 4:42 a.m.
Created at: March 30, 2026, 6:38 p.m.