Triple

T32139287
Position Surface form Disambiguated ID Type / Status
Subject Verilog-AMS E820858 entity
Predicate typicalToolSupport P29151 FINISHED
Object Synopsys CustomSim
Synopsys CustomSim is a high-performance circuit simulation tool from Synopsys used for accurate analog, mixed-signal, and transistor-level verification in advanced IC design.
 E1993665 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: Synopsys CustomSim | Statement: [Verilog-AMS, typicalToolSupport, Synopsys CustomSim]
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: Synopsys CustomSim
Triple: [Verilog-AMS, typicalToolSupport, Synopsys CustomSim]
Generated description
Synopsys CustomSim is a high-performance circuit simulation tool from Synopsys used for accurate analog, mixed-signal, and transistor-level verification in advanced IC design.

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_69f349039e0c819091c7a7d322e3f46d completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69ffdd7706b88190870046670d46b397 completed May 10, 2026, 1:20 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2f013da36481908c18124d4143f973 completed June 14, 2026, 7:30 p.m.
NEDg Description generation batch_6a2f01fe7008819091d5a73abe7ea366 completed June 14, 2026, 7:33 p.m.
NED2 Entity disambiguation (via description) batch_6a2f035270508190bff756fef3523976 completed June 14, 2026, 7:38 p.m.
Created at: May 1, 2026, 12:30 a.m.