Triple

T35140510
Position Surface form Disambiguated ID Type / Status
Subject IFIP Technical Assembly E1014690 entity
Predicate hasMember P10 FINISHED
Object IFIP Technical Committee 10
IFIP Technical Committee 10 is a specialist committee within the International Federation for Information Processing focused on research and standardization in computer systems technology.
E2143405 NE FINISHED

How this triple was built (2 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: IFIP Technical Committee 10 | Statement: [IFIP Technical Assembly, hasMember, IFIP Technical Committee 10]
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: IFIP Technical Committee 10
Triple: [IFIP Technical Assembly, hasMember, IFIP Technical Committee 10]
Generated description
IFIP Technical Committee 10 is a specialist committee within the International Federation for Information Processing focused on research and standardization in computer systems technology.

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_69f76dd9c1848190af70d4882a2c1ad7 completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f78ca911e081909d27bfe766d555d3 completed May 3, 2026, 5:58 p.m.
NED1 Entity disambiguation (via context triple) batch_6a384a17a34c8190915cf4f0778d6a7d completed June 21, 2026, 8:31 p.m.
NEDg Description generation batch_6a384af0370c8190b49b96626cfb98c2 completed June 21, 2026, 8:34 p.m.
NED2 Entity disambiguation (via description) batch_6a384b839a308190a63708ae678946da completed June 21, 2026, 8:37 p.m.
Created at: May 3, 2026, 4:02 p.m.