Triple

T13092870
Position Surface form Disambiguated ID Type / Status
Subject Lea Green E310506 entity
Predicate hasStationCode P1289 FINISHED
Object LEG
LEG is the National Rail station code for Lea Green railway station in Merseyside, England.
 E1019496 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: LEG | Statement: [Lea Green, hasStationCode, LEG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LEG
Context triple: [Lea Green, hasStationCode, LEG]
  • A. LEG
    LEG is the International Maritime Organization’s Legal Committee, responsible for developing and maintaining international maritime law and liability conventions.
  • B. LEG
    LEG is the commonly used abbreviation for the Faculty of Law, Economics and Governance at Utrecht University, which combines legal, economic and governance disciplines.
  • C. LEM
    LEM is the original abbreviation for the Apollo Lunar Module, the spacecraft used by NASA astronauts to land on and ascend from the Moon during the Apollo missions.
  • D. LAG
    LAG is the commonly used abbreviation for the LA Galaxy, a professional Major League Soccer club based in the Los Angeles area.
  • E. LEV
    LEV is the vehicle registration code used on license plates for the German city of Leverkusen.
  • 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: LEG
Triple: [Lea Green, hasStationCode, LEG]
Generated description
LEG is the National Rail station code for Lea Green railway station in Merseyside, England.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LEG
Target entity description: LEG is the National Rail station code for Lea Green railway station in Merseyside, England.
  • A. LEG
    LEG is the International Maritime Organization’s Legal Committee, responsible for developing and maintaining international maritime law and liability conventions.
  • B. LEG
    LEG is the commonly used abbreviation for the Faculty of Law, Economics and Governance at Utrecht University, which combines legal, economic and governance disciplines.
  • C. LEM
    LEM is the original abbreviation for the Apollo Lunar Module, the spacecraft used by NASA astronauts to land on and ascend from the Moon during the Apollo missions.
  • D. LAG
    LAG is the commonly used abbreviation for the LA Galaxy, a professional Major League Soccer club based in the Los Angeles area.
  • E. LEV
    LEV is the vehicle registration code used on license plates for the German city of Leverkusen.
  • 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_69d806a733548190989cfd4ce981ca33 completed April 9, 2026, 8:05 p.m.
NER Named-entity recognition batch_69d9813acbac8190b2fe5e07287457cf completed April 10, 2026, 11:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6d617f1908190a2fa147bedede54f completed May 3, 2026, 4:59 a.m.
NEDg Description generation batch_69f6d6e326408190b7906c7ea8e3ef85 completed May 3, 2026, 5:02 a.m.
NED2 Entity disambiguation (via description) batch_69f6d873b978819097962c82e8ffdac8 completed May 3, 2026, 5:09 a.m.
Created at: April 9, 2026, 9:03 p.m.