Triple

T27748571
Position Surface form Disambiguated ID Type / Status
Subject Jacobi's theorem on determinants E702054 entity
Predicate relatedTo P37 FINISHED
Object Cramer's rule
Cramer's rule is a linear algebra method that solves systems of linear equations using ratios of determinants of coefficient matrices.
 E1786973 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: Cramer's rule | Statement: [Jacobi's theorem on determinants, relatedTo, Cramer's rule]
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: Cramer's rule
Triple: [Jacobi's theorem on determinants, relatedTo, Cramer's rule]
Generated description
Cramer's rule is a linear algebra method that solves systems of linear equations using ratios of determinants of coefficient matrices.

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_69ef6a53c7388190899baa6daf42301c completed April 27, 2026, 1:53 p.m.
NER Named-entity recognition batch_69f6371c6454819099f05f09d60a374c completed May 2, 2026, 5:40 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12e47db99081909b438955637aa296 completed May 24, 2026, 11:43 a.m.
NEDg Description generation batch_6a12e5970df88190923224d331fcb41d completed May 24, 2026, 11:48 a.m.
NED2 Entity disambiguation (via description) batch_6a12e6cfffd4819098e7fc01a09f2a7c completed May 24, 2026, 11:53 a.m.
Created at: April 27, 2026, 4:18 p.m.