Triple

T24793328
Position Surface form Disambiguated ID Type / Status
Subject Isaak Pomeranchuk E620310 entity
Predicate notableWork P4 FINISHED
Object Pomeranchuk theorem
The Pomeranchuk theorem is a result in high-energy physics that predicts the equality of particle and antiparticle total cross-sections at asymptotically high energies under certain general assumptions.
 E1654473 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: Pomeranchuk theorem | Statement: [Isaak Pomeranchuk, notableWork, Pomeranchuk theorem]
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: Pomeranchuk theorem
Triple: [Isaak Pomeranchuk, notableWork, Pomeranchuk theorem]
Generated description
The Pomeranchuk theorem is a result in high-energy physics that predicts the equality of particle and antiparticle total cross-sections at asymptotically high energies under certain general assumptions.

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_69e2fabe77c8819085f7ce6486248139 completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f41104fdd48190aa8c879738d8845b completed May 1, 2026, 2:33 a.m.
NED1 Entity disambiguation (via context triple) batch_6a101c2d97608190b8ddc47d6cf9ce4a completed May 22, 2026, 9:04 a.m.
NEDg Description generation batch_6a1028143b4c8190b89ad73aecb56e0d completed May 22, 2026, 9:55 a.m.
NED2 Entity disambiguation (via description) batch_6a1029a084708190a87c7d2add8f4688 completed May 22, 2026, 10:02 a.m.
Created at: April 18, 2026, 4:47 a.m.