Triple

T1754366
Position Surface form Disambiguated ID Type / Status
Subject Orpington railway station E38517 entity
Predicate hasStationCode P1289 FINISHED
Object ORP
ORP is the National Rail station code for Orpington railway station in the London Borough of Bromley, England.
 E196113 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: ORP | Statement: [Orpington railway station, hasStationCode, ORP]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ORP
Context triple: [Orpington railway station, hasStationCode, ORP]
  • A. ORCP
    ORCP is the standard legal abbreviation for the Oregon Rules of Civil Procedure, which govern civil court proceedings in the state of Oregon.
  • B. ORC
    ORC (Optimized Row Columnar) is a highly efficient, columnar storage file format commonly used in big data systems to enable fast analytics and compression.
  • C. Ors
    Ors is a small commune in northern France, known for its World War I history and military cemetery.
  • D. OAR
    OAR is the commonly used abbreviation for the Oregon Administrative Rules, which comprise the codified regulations issued by Oregon’s state agencies.
  • E. OAR
    OAR is the commonly used acronym for the U.S. Environmental Protection Agency’s Office of Air and Radiation, which oversees national efforts to protect and improve air quality and control radiation exposure.
  • 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: ORP
Triple: [Orpington railway station, hasStationCode, ORP]
Generated description
ORP is the National Rail station code for Orpington railway station in the London Borough of Bromley, England.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: ORP
Target entity description: ORP is the National Rail station code for Orpington railway station in the London Borough of Bromley, England.
  • A. ORCP
    ORCP is the standard legal abbreviation for the Oregon Rules of Civil Procedure, which govern civil court proceedings in the state of Oregon.
  • B. ORC
    ORC (Optimized Row Columnar) is a highly efficient, columnar storage file format commonly used in big data systems to enable fast analytics and compression.
  • C. Ors
    Ors is a small commune in northern France, known for its World War I history and military cemetery.
  • D. OAR
    OAR is the commonly used acronym for the U.S. Environmental Protection Agency’s Office of Air and Radiation, which oversees national efforts to protect and improve air quality and control radiation exposure.
  • E. OAR
    OAR is the commonly used abbreviation for the Oregon Administrative Rules, which comprise the codified regulations issued by Oregon’s state agencies.
  • 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_69a8862bdb2081908aefe831c8aa8017 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa641841748190ad05cac4a27cced9 completed March 6, 2026, 5:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69ada0e84c1c8190917edf14003cba81 completed March 8, 2026, 4:16 p.m.
NEDg Description generation batch_69ada1a401548190af00bae3b89e46b0 completed March 8, 2026, 4:19 p.m.
NED2 Entity disambiguation (via description) batch_69ada26804288190838a93b71b494650 completed March 8, 2026, 4:23 p.m.
Created at: March 4, 2026, 7:31 p.m.