Triple

T35477002

Position	Surface form	Disambiguated ID	Type / Status
Subject	Bill Tutte	`E1025361`	entity
Predicate	notableConcept	`P201`	FINISHED
Object	Tutte–Berge formula The Tutte–Berge formula is a fundamental result in graph theory that characterizes the size of a maximum matching in a finite graph via a minimization over vertex subsets.	`E2142309`	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: Tutte–Berge formula | Statement: [Bill Tutte, notableConcept, Tutte–Berge formula]

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: Tutte–Berge formula
Triple: [Bill Tutte, notableConcept, Tutte–Berge formula]

Generated description

The Tutte–Berge formula is a fundamental result in graph theory that characterizes the size of a maximum matching in a finite graph via a minimization over vertex subsets.

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_69f76dfadba0819083456aadcd6864ea`	completed	May 3, 2026, 3:47 p.m.
NER	Named-entity recognition	`batch_69f796e951b08190854327f83932c83c`	completed	May 3, 2026, 6:41 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a38403ba6808190bd62e228cc5d827f`	completed	June 21, 2026, 7:49 p.m.
NEDg	Description generation	`batch_6a3841a972e8819090d7e0a6d0f10aac`	completed	June 21, 2026, 7:55 p.m.
NED2	Entity disambiguation (via description)	`batch_6a38420fb2a88190ac17badf0bfc5b6c`	completed	June 21, 2026, 7:57 p.m.

Created at: May 3, 2026, 4:04 p.m.