Triple

T2114048
Position Surface form Disambiguated ID Type / Status
Subject ASN.1 E42565 entity
Predicate hasEncodingRule P33682 FINISHED
Object GSER
GSER (Generic String Encoding Rules) is a textual encoding format for ASN.1 data structures designed to represent values in a human-readable string form.
 E235257 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: GSER | Statement: [ASN.1, hasEncodingRule, GSER]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: GSER
Context triple: [ASN.1, hasEncodingRule, GSER]
  • A. Gers
    Gers is a river in southwestern France that flows through the historical region of Gascony before joining the Garonne.
  • B. GER
    GER is the official FIFA country code used to represent the Germany national football team in international competitions and records.
  • C. GS
    GS is the two-letter ISO 3166 country code assigned to the British Overseas Territory of South Georgia and the South Sandwich Islands in the southern Atlantic Ocean.
  • D. GSB
    GSB is Stanford University's renowned graduate business school, offering MBA and other advanced management programs and known for its innovation, entrepreneurship focus, and global impact.
  • E. BGer
    BGer is the commonly used German abbreviation for the Federal Supreme Court of Switzerland, the country's highest judicial authority.
  • 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: GSER
Triple: [ASN.1, hasEncodingRule, GSER]
Generated description
GSER (Generic String Encoding Rules) is a textual encoding format for ASN.1 data structures designed to represent values in a human-readable string form.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: GSER
Target entity description: GSER (Generic String Encoding Rules) is a textual encoding format for ASN.1 data structures designed to represent values in a human-readable string form.
  • A. Gers
    Gers is a river in southwestern France that flows through the historical region of Gascony before joining the Garonne.
  • B. GER
    GER is the official FIFA country code used to represent the Germany national football team in international competitions and records.
  • C. GS
    GS is the two-letter ISO 3166 country code assigned to the British Overseas Territory of South Georgia and the South Sandwich Islands in the southern Atlantic Ocean.
  • D. GSB
    GSB is Stanford University's renowned graduate business school, offering MBA and other advanced management programs and known for its innovation, entrepreneurship focus, and global impact.
  • E. BGer
    BGer is the commonly used German abbreviation for the Federal Supreme Court of Switzerland, the country's highest judicial authority.
  • 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_69a8871040f08190aac2e2d0ab6b47ad completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abbdc3a12081908e95ae870207367f completed March 7, 2026, 5:55 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae30748a7c81908ab3e08b7aa9900a completed March 9, 2026, 2:29 a.m.
NEDg Description generation batch_69ae3197794c81908530b26fcb8a4a77 completed March 9, 2026, 2:33 a.m.
NED2 Entity disambiguation (via description) batch_69ae31f65444819090c2af22c21ec3e1 completed March 9, 2026, 2:35 a.m.
Created at: March 4, 2026, 7:43 p.m.