Triple

T35859865
Position Surface form Disambiguated ID Type / Status
Subject Tayside Police E1036915 entity
Predicate usedVehicleLivery P21786 FINISHED
Object Battenburg markings
Battenburg markings are a high-visibility, block-pattern livery commonly used on emergency service vehicles in the UK and other countries to improve their conspicuity and recognizability.
 E2157921 NE FINISHED

How this triple was built (3 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: Battenburg markings | Statement: [Tayside Police, usedVehicleLivery, Battenburg markings]
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: Battenburg markings
Triple: [Tayside Police, usedVehicleLivery, Battenburg markings]
Generated description
Battenburg markings are a high-visibility, block-pattern livery commonly used on emergency service vehicles in the UK and other countries to improve their conspicuity and recognizability.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: usedVehicleLivery
Context triple: [Tayside Police, usedVehicleLivery, Battenburg markings]
  • A. hasLivery chosen
    Indicates that one entity bears or displays the distinctive colors, markings, or branding (livery) associated with another entity.
  • B. liveryFeature
    Indicates a characteristic or design element that is part of a specific livery or external appearance scheme.
  • C. liveryColors
    Indicates the specific set of colors used as the official or characteristic color scheme associated with an entity (such as a brand, organization, or vehicle).
  • D. usesVehicleVariant
    Indicates that one entity performs an action or function by employing a specific variant or version of a vehicle.
  • E. liveryInspiredBy
    Indicates that one livery’s design, colors, or overall appearance is based on, influenced by, or pays homage to another livery.
  • F. None of above.

Provenance (6 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_69f76e1d279c8190843e5b64a0a12c3f completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7aa3883d48190b05e3d2da7a017ae completed May 3, 2026, 8:04 p.m.
NED1 Entity disambiguation (via context triple) batch_6a389c3510308190b8109f8e670e0642 completed June 22, 2026, 2:21 a.m.
NEDg Description generation batch_6a389cfaa1fc81908e382b4befed99f6 completed June 22, 2026, 2:24 a.m.
NED2 Entity disambiguation (via description) batch_6a389d9dcbd88190b86408dcb14f8128 completed June 22, 2026, 2:27 a.m.
PD Predicate disambiguation batch_69f7a8d435288190b30b1991fb003121 completed May 3, 2026, 7:58 p.m.
Created at: May 3, 2026, 4:06 p.m.