Triple

T1821672
Position Surface form Disambiguated ID Type / Status
Subject CP/M E40551 entity
Predicate hasDefaultShellCommand P15534 FINISHED
Object ED
ED is a classic line-based text editor commonly used in Unix-like operating systems, known for its minimal interface and suitability for scripting and low-resource environments.
E202919 NE FINISHED

How this triple was built (4 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: ED | Statement: [CP/M, hasDefaultShellCommand, ED]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ED
Context triple: [CP/M, hasDefaultShellCommand, ED]
  • A. ED
    ED is the federal agency responsible for establishing policy, administering, and coordinating most education-related programs in the United States.
  • B. Ed
    Ed is a common masculine given name, typically used as a short form of names such as Edward, Edwin, or Edmund.
  • C. EC
    EC is the two-letter ISO 3166-1 alpha-2 country code assigned to Ecuador.
  • D. E
    The E is a New York City Subway line that runs between Queens and Manhattan, providing a key rapid transit connection used by AirTrain JFK passengers traveling to and from the city.
  • E. E
    E is the letter designation for the E branch of Boston’s MBTA Green Line light rail service.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: ED
Triple: [CP/M, hasDefaultShellCommand, ED]
Generated description
ED is a classic line-based text editor commonly used in Unix-like operating systems, known for its minimal interface and suitability for scripting and low-resource environments.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: ED
Target entity description: ED is a classic line-based text editor commonly used in Unix-like operating systems, known for its minimal interface and suitability for scripting and low-resource environments.
  • A. ED
    ED is the federal agency responsible for establishing policy, administering, and coordinating most education-related programs in the United States.
  • B. Ed
    Ed is a common masculine given name, typically used as a short form of names such as Edward, Edwin, or Edmund.
  • C. EC
    EC is the two-letter ISO 3166-1 alpha-2 country code assigned to Ecuador.
  • D. E
    E is the stage name of Mark Oliver Everett, the singer-songwriter and multi-instrumentalist best known as the frontman of the indie rock band Eels.
  • E. E
    The E is a New York City Subway line that runs between Queens and Manhattan, providing a key rapid transit connection used by AirTrain JFK passengers traveling to and from the city.
  • F. None of above. chosen

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_69a8864526c081908a3a4d74f689e2c5 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb0003d308190a024f8c03c5f5dad completed March 7, 2026, 4:56 a.m.
NED1 Entity disambiguation (via context triple) batch_69adbf6526f88190907de68a5344a084 completed March 8, 2026, 6:26 p.m.
NEDg Description generation batch_69adbff32038819088dffc71e8376821 completed March 8, 2026, 6:29 p.m.
NED2 Entity disambiguation (via description) batch_69adc084c9b88190a0d53f5c7c459611 completed March 8, 2026, 6:31 p.m.
Created at: March 4, 2026, 7:32 p.m.