Triple

T14839675
Position Surface form Disambiguated ID Type / Status
Subject Saint-Germain-en-Laye RER station E348925 entity
Predicate hasStationCode P1289 FINISHED
Object SGL
SGL is the station code for the Saint-Germain-en-Laye terminus of Paris RER line A in the western suburbs of Paris.
 E446820 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: SGL | Statement: [Saint-Germain-en-Laye RER station, hasStationCode, SGL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SGL
Context triple: [Saint-Germain-en-Laye RER station, hasStationCode, SGL]
  • A. SGL
    SGL is the station code for Singleton railway station, a train stop serving the locality of Singleton.
  • B. SBGL
    SBGL is the ICAO airport code for Rio de Janeiro–Galeão International Airport, a major international gateway serving Rio de Janeiro, Brazil.
  • C. SG
    SG is a postcode area in the United Kingdom covering parts of Hertfordshire and surrounding regions.
  • D. SG
    SG is the vehicle registration code used on license plates for the Swiss canton of St. Gallen.
  • E. SG
    SG (Sanspareils Greenlands) is a prominent Indian sports equipment manufacturer best known for its high-quality cricket gear, including bats, balls, and protective equipment.
  • 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: SGL
Triple: [Saint-Germain-en-Laye RER station, hasStationCode, SGL]
Generated description
SGL is the station code for the Saint-Germain-en-Laye terminus of Paris RER line A in the western suburbs of Paris.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SGL
Target entity description: SGL is the station code for the Saint-Germain-en-Laye terminus of Paris RER line A in the western suburbs of Paris.
  • A. SGL chosen
    SGL is the station code for Singleton railway station, a train stop serving the locality of Singleton.
  • B. SBGL
    SBGL is the ICAO airport code for Rio de Janeiro–Galeão International Airport, a major international gateway serving Rio de Janeiro, Brazil.
  • C. SG
    SG is a postcode area in the United Kingdom covering parts of Hertfordshire and surrounding regions.
  • D. SG
    SG is the vehicle registration code used on license plates for the Swiss canton of St. Gallen.
  • E. SG
    SG is the vehicle registration code used on license plates for cars registered in Gliwice, Poland.
  • F. None of above.

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_69d822ec69008190a9232caa68836872 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69ded28e40f08190b309d8ac6404d2fc completed April 14, 2026, 11:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe38a9eb9481908ca509f484007cf6 completed May 8, 2026, 7:25 p.m.
NEDg Description generation batch_69fe3d0eca948190b107bc593b6e5b72 completed May 8, 2026, 7:44 p.m.
NED2 Entity disambiguation (via description) batch_69fe3d94785881908911a7c6f1546d45 completed May 8, 2026, 7:46 p.m.
Created at: April 10, 2026, 1:53 a.m.