Triple

T850752
Position Surface form Disambiguated ID Type / Status
Subject MBTA Green Line E branch E18379 entity
Predicate lineLetter P5539 FINISHED
Object E
E is the letter designation for the E branch of Boston’s MBTA Green Line light rail service.
 E99403 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: E | Statement: [MBTA Green Line E branch, lineLetter, E]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: E
Context triple: [MBTA Green Line E branch, lineLetter, E]
  • A. 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.
  • B. EC
    EC is the two-letter ISO 3166-1 alpha-2 country code assigned to Ecuador.
  • C. ED
    ED is the federal agency responsible for establishing policy, administering, and coordinating most education-related programs in the United States.
  • D. EG
    EG is the standard abbreviation for the Egmont Group, an international network of Financial Intelligence Units that collaborates to combat money laundering and terrorist financing.
  • E. D
    D is a statically typed, compiled systems programming language designed as a modern successor to C and C++, emphasizing high performance, safety features, and programmer productivity.
  • 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: E
Triple: [MBTA Green Line E branch, lineLetter, E]
Generated description
E is the letter designation for the E branch of Boston’s MBTA Green Line light rail service.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: E
Target entity description: E is the letter designation for the E branch of Boston’s MBTA Green Line light rail service.
  • A. 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.
  • B. EC
    EC is the two-letter ISO 3166-1 alpha-2 country code assigned to Ecuador.
  • C. ED
    ED is the federal agency responsible for establishing policy, administering, and coordinating most education-related programs in the United States.
  • D. EG
    EG is the standard abbreviation for the Egmont Group, an international network of Financial Intelligence Units that collaborates to combat money laundering and terrorist financing.
  • E. D
    D is a statically typed, compiled systems programming language designed as a modern successor to C and C++, emphasizing high performance, safety features, and programmer productivity.
  • 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_69a4938b04208190b82e1df6b572c548 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4ac215194819099e6bc1b5df58fb3 completed March 1, 2026, 9:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69a792a0666c8190bfc9166d45b4e867 completed March 4, 2026, 2:02 a.m.
NEDg Description generation batch_69a793563cc881909381f898f240c0bd completed March 4, 2026, 2:05 a.m.
NED2 Entity disambiguation (via description) batch_69a7941add588190913198a7f7b20943 completed March 4, 2026, 2:08 a.m.
Created at: March 1, 2026, 7:38 p.m.