Triple

T28411425
Position Surface form Disambiguated ID Type / Status
Subject Kemeny–Young method E719672 entity
Predicate relatedConcept P37 FINISHED
Object Rank aggregation problem
The rank aggregation problem is a computational task of combining multiple individual rankings into a single consensus ranking that best reflects the collective preferences.
 E1816924 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: Rank aggregation problem | Statement: [Kemeny–Young method, relatedConcept, Rank aggregation problem]
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: Rank aggregation problem
Triple: [Kemeny–Young method, relatedConcept, Rank aggregation problem]
Generated description
The rank aggregation problem is a computational task of combining multiple individual rankings into a single consensus ranking that best reflects the collective preferences.

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_69eff6f0f37c8190b37bc6fab08a9449 completed April 27, 2026, 11:53 p.m.
NER Named-entity recognition batch_69f64dbc9be4819082633afaeb8136dc completed May 2, 2026, 7:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1633164af08190afff502873b1e54b completed May 26, 2026, 11:56 p.m.
NEDg Description generation batch_6a1633dd88848190bf73982c2ce00ffd completed May 26, 2026, 11:59 p.m.
NED2 Entity disambiguation (via description) batch_6a16365587e08190b93663807e950946 completed May 27, 2026, 12:09 a.m.
Created at: April 28, 2026, 1:27 a.m.