Triple

T3728482
Position Surface form Disambiguated ID Type / Status
Subject Azerbaijan National Academy of Sciences E79005 entity
Predicate abbreviation P43 FINISHED
Object ANAS
ANAS is the primary state research institution in Azerbaijan, overseeing and coordinating scientific activities and academic research across the country.
 E383795 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: ANAS | Statement: [Azerbaijan National Academy of Sciences, abbreviation, ANAS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ANAS
Context triple: [Azerbaijan National Academy of Sciences, abbreviation, ANAS]
  • A. ANE
    ANE is Apple's dedicated on-device neural processing unit designed to accelerate machine learning tasks efficiently on Apple hardware.
  • B. Anat
    Anat is a prominent Canaanite war and fertility goddess known for her fierce martial prowess and protective role in the ancient Levantine pantheon.
  • C. Asan
    Asan is a city in South Korea known for its hot springs, historical sites, and growing role as an industrial and educational center.
  • D. Anif
    Anif is a small Austrian municipality near Salzburg, known for its historic castle and as a residence of notable figures.
  • E. ANA
    ANA is the standard three-letter abbreviation used for the Anaheim Ducks, a professional ice hockey team in the National Hockey League.
  • 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: ANAS
Triple: [Azerbaijan National Academy of Sciences, abbreviation, ANAS]
Generated description
ANAS is the primary state research institution in Azerbaijan, overseeing and coordinating scientific activities and academic research across the country.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ANAS
Target entity description: ANAS is the primary state research institution in Azerbaijan, overseeing and coordinating scientific activities and academic research across the country.
  • A. ANE
    ANE is Apple's dedicated on-device neural processing unit designed to accelerate machine learning tasks efficiently on Apple hardware.
  • B. Anat
    Anat is a prominent Canaanite war and fertility goddess known for her fierce martial prowess and protective role in the ancient Levantine pantheon.
  • C. Asan
    Asan is a city in South Korea known for its hot springs, historical sites, and growing role as an industrial and educational center.
  • D. Anif
    Anif is a small Austrian municipality near Salzburg, known for its historic castle and as a residence of notable figures.
  • E. ANA
    ANA is the standard three-letter abbreviation used for the Anaheim Ducks, a professional ice hockey team in the National Hockey League.
  • 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_69ad8b0e4650819090ad7cef094285e8 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcaf921bc81908bb347d6b9204670 completed March 8, 2026, 7:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4db13d34881909fa74c682184b797 completed March 14, 2026, 3:50 a.m.
NEDg Description generation batch_69b4dbded2c8819084c26ae2ec1c19b3 completed March 14, 2026, 3:54 a.m.
NED2 Entity disambiguation (via description) batch_69b4dc9b80f8819083074657a32798a4 completed March 14, 2026, 3:57 a.m.
Created at: March 8, 2026, 3:34 p.m.