Triple

T25063628
Position Surface form Disambiguated ID Type / Status
Subject Puiseux series E627726 entity
Predicate usedFor P98 FINISHED
Object Newton–Puiseux algorithm
The Newton–Puiseux algorithm is a method in algebraic geometry and singularity theory for computing Puiseux series expansions of algebraic functions near singular points.
 E1661530 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: Newton–Puiseux algorithm | Statement: [Puiseux series, usedFor, Newton–Puiseux algorithm]
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: Newton–Puiseux algorithm
Triple: [Puiseux series, usedFor, Newton–Puiseux algorithm]
Generated description
The Newton–Puiseux algorithm is a method in algebraic geometry and singularity theory for computing Puiseux series expansions of algebraic functions near singular points.

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_69e2ff2d71dc8190b4758e57d643cbe4 completed April 18, 2026, 3:49 a.m.
NER Named-entity recognition batch_69f4599b7ff081909862edeae57c2c2d completed May 1, 2026, 7:43 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1048d54f5c81908328cb676bd4ef4d completed May 22, 2026, 12:15 p.m.
NEDg Description generation batch_6a1049b747c48190a0b61cbd96172411 completed May 22, 2026, 12:19 p.m.
NED2 Entity disambiguation (via description) batch_6a104a868810819098fc6286e7599ea0 completed May 22, 2026, 12:22 p.m.
Created at: April 18, 2026, 6:10 a.m.