Triple

T785594
Position Surface form Disambiguated ID Type / Status
Subject G train E16593 entity
Predicate originalName P65 FINISHED
Object GG
GG was the original designation for New York City's G subway service, a crosstown line that runs through Brooklyn and Queens without entering Manhattan.
 E92816 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: GG | Statement: [G train, originalName, GG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: GG
Context triple: [G train, originalName, GG]
  • A. GU
    GU is the two-letter ISO 3166 country code assigned to Guam, an unincorporated territory of the United States in the western Pacific Ocean.
  • B. GN
    GN is a fast, meta-build system tool used primarily by the Chromium project to generate build files for Ninja.
  • C. 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.
  • D. GER
    GER is the official FIFA country code used to represent the Germany national football team in international competitions and records.
  • E. Ga
    Ga is a Kwa language spoken primarily by the Ga people in and around Accra, the capital region of Ghana.
  • 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: GG
Triple: [G train, originalName, GG]
Generated description
GG was the original designation for New York City's G subway service, a crosstown line that runs through Brooklyn and Queens without entering Manhattan.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: GG
Target entity description: GG was the original designation for New York City's G subway service, a crosstown line that runs through Brooklyn and Queens without entering Manhattan.
  • A. GU
    GU is the two-letter ISO 3166 country code assigned to Guam, an unincorporated territory of the United States in the western Pacific Ocean.
  • B. GN
    GN is a fast, meta-build system tool used primarily by the Chromium project to generate build files for Ninja.
  • C. 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.
  • D. GER
    GER is the official FIFA country code used to represent the Germany national football team in international competitions and records.
  • E. Ga
    Ga is a Kwa language spoken primarily by the Ga people in and around Accra, the capital region of Ghana.
  • 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_69a4936ad1fc81908f190208059ccf78 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a76b0d6c8190a09b1a0bd4a6eeec completed March 1, 2026, 8:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69a6733e47148190bad0ac71cf45dd85 completed March 3, 2026, 5:35 a.m.
NEDg Description generation batch_69a673afbd308190863aea2ff0f650cb completed March 3, 2026, 5:37 a.m.
NED2 Entity disambiguation (via description) batch_69a6743f50d0819089885836b9668466 completed March 3, 2026, 5:40 a.m.
Created at: March 1, 2026, 7:38 p.m.