Triple

T27653863
Position Surface form Disambiguated ID Type / Status
Subject Osborne Reynolds E696936 entity
Predicate knownFor P22 FINISHED
Object Reynolds analogy
Reynolds analogy is a concept in fluid mechanics that relates momentum transfer to heat transfer in turbulent flow, providing a simplified way to connect friction factors with heat-transfer coefficients.
 E1784104 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: Reynolds analogy | Statement: [Osborne Reynolds, knownFor, Reynolds analogy]
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: Reynolds analogy
Triple: [Osborne Reynolds, knownFor, Reynolds analogy]
Generated description
Reynolds analogy is a concept in fluid mechanics that relates momentum transfer to heat transfer in turbulent flow, providing a simplified way to connect friction factors with heat-transfer coefficients.

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_69ef590abd3c8190834d0193bde12007 completed April 27, 2026, 12:39 p.m.
NER Named-entity recognition batch_69f631d6d92c8190bc9e523546c53d96 completed May 2, 2026, 5:18 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12da99a7088190adae6a011d941654 completed May 24, 2026, 11:01 a.m.
NEDg Description generation batch_6a12db2833688190af921e97c6e5d05d completed May 24, 2026, 11:04 a.m.
NED2 Entity disambiguation (via description) batch_6a12db977df48190b71bce8408b51269 completed May 24, 2026, 11:05 a.m.
Created at: April 27, 2026, 2:33 p.m.