Triple

T32667439
Position Surface form Disambiguated ID Type / Status
Subject Ornstein–Zernike equation E835194 entity
Predicate basisFor P125 FINISHED
Object Percus–Yevick approximation
The Percus–Yevick approximation is a widely used integral equation approach in statistical mechanics for estimating the structure and thermodynamic properties of classical fluids, particularly hard-sphere systems.
 E2017435 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: Percus–Yevick approximation | Statement: [Ornstein–Zernike equation, basisFor, Percus–Yevick approximation]
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: Percus–Yevick approximation
Triple: [Ornstein–Zernike equation, basisFor, Percus–Yevick approximation]
Generated description
The Percus–Yevick approximation is a widely used integral equation approach in statistical mechanics for estimating the structure and thermodynamic properties of classical fluids, particularly hard-sphere systems.

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_69f349303ccc8190a70d0f6e8a21d3fb completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6c7aa36f88190b971c98682535c86 completed May 3, 2026, 3:57 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3492b985248190966229cb90207f65 completed June 19, 2026, 12:52 a.m.
NEDg Description generation batch_6a3493499a58819095b80fc358562bee completed June 19, 2026, 12:54 a.m.
NED2 Entity disambiguation (via description) batch_6a34960f294081908b8197b8f527b0cd completed June 19, 2026, 1:06 a.m.
Created at: May 1, 2026, 1:08 a.m.