Triple

T27733274
Position Surface form Disambiguated ID Type / Status
Subject System.IFormatProvider E697477 entity
Predicate usedBy P260 FINISHED
Object System.Parse methods
System.Parse methods are .NET framework routines that convert string representations of values into strongly typed data such as numbers, dates, and other primitives, often using culture-specific formatting rules.
 E1786507 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: System.Parse methods | Statement: [System.IFormatProvider, usedBy, System.Parse methods]
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: System.Parse methods
Triple: [System.IFormatProvider, usedBy, System.Parse methods]
Generated description
System.Parse methods are .NET framework routines that convert string representations of values into strongly typed data such as numbers, dates, and other primitives, often using culture-specific formatting rules.

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_69ef590c3e288190ad54d2465af8ca4e completed April 27, 2026, 12:39 p.m.
NER Named-entity recognition batch_69f6369e16a08190853fdd8ac05800f6 completed May 2, 2026, 5:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12e4732d888190a3680cbdc50398be completed May 24, 2026, 11:43 a.m.
NEDg Description generation batch_6a12e4fca4088190b20187243cbf974e completed May 24, 2026, 11:46 a.m.
NED2 Entity disambiguation (via description) batch_6a12e606bc688190958b5e84777566fb completed May 24, 2026, 11:50 a.m.
Created at: April 27, 2026, 3:12 p.m.