Triple

T15628824
Position Surface form Disambiguated ID Type / Status
Subject Green Lane railway station E375753 entity
Predicate code P1537 FINISHED
Object GNL
GNL is the National Rail station code for Green Lane railway station in Merseyside, England.
 E1167565 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: GNL | Statement: [Green Lane railway station, code, GNL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: GNL
Context triple: [Green Lane railway station, code, GNL]
  • A. GN
    GN is a fast, meta-build system tool used primarily by the Chromium project to generate build files for Ninja.
  • B. GNF
    GNF is the official currency code for the Guinean franc, the national currency of Guinea.
  • C. GNA
    GNA is the acronym for the Argentine National Gendarmerie, a federal security force responsible for border protection, rural security, and supporting national law enforcement in Argentina.
  • D. GL
    GL is the stock ticker symbol for Globe Life Inc., a U.S.-based life and health insurance company traded on the New York Stock Exchange.
  • E. GL
    GL is the postcode area in the United Kingdom that covers Cheltenham and surrounding parts of Gloucestershire.
  • 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: GNL
Triple: [Green Lane railway station, code, GNL]
Generated description
GNL is the National Rail station code for Green Lane railway station in Merseyside, England.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: GNL
Target entity description: GNL is the National Rail station code for Green Lane railway station in Merseyside, England.
  • A. GN
    GN is a fast, meta-build system tool used primarily by the Chromium project to generate build files for Ninja.
  • B. GNF
    GNF is the official currency code for the Guinean franc, the national currency of Guinea.
  • C. GNA
    GNA is the acronym for the Argentine National Gendarmerie, a federal security force responsible for border protection, rural security, and supporting national law enforcement in Argentina.
  • D. GL
    GL is the postcode area in the United Kingdom that covers Cheltenham and surrounding parts of Gloucestershire.
  • E. GL
    GL is the stock ticker symbol for Globe Life Inc., a U.S.-based life and health insurance company traded on the New York Stock Exchange.
  • 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_69d85cd035a48190b73d5579ab73969a completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04eb4301881908c7157227fdf79b6 completed April 16, 2026, 2:51 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff5f43191c81908c5704314a002608 completed May 9, 2026, 4:22 p.m.
NEDg Description generation batch_69ff5ffaefb4819094468ff0008740f8 completed May 9, 2026, 4:25 p.m.
NED2 Entity disambiguation (via description) batch_69ff6062f0ac819081270f270ce2f057 completed May 9, 2026, 4:27 p.m.
Created at: April 10, 2026, 4:14 a.m.