Triple

T25708725
Position Surface form Disambiguated ID Type / Status
Subject Volkspolizei E644669 entity
Predicate usedVehicleModel P159190 FINISHED
Object Wartburg patrol cars
Wartburg patrol cars were East German-made police vehicles commonly used by the Volkspolizei during the Cold War era.
 E1689445 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: Wartburg patrol cars | Statement: [Volkspolizei, usedVehicleModel, Wartburg patrol cars]
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: Wartburg patrol cars
Triple: [Volkspolizei, usedVehicleModel, Wartburg patrol cars]
Generated description
Wartburg patrol cars were East German-made police vehicles commonly used by the Volkspolizei during the Cold War era.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: usedVehicleModel
Context triple: [Volkspolizei, usedVehicleModel, Wartburg patrol cars]
  • A. carModel
    Indicates the specific model designation of a car within a particular make or brand.
  • B. vehicleUsed
    Indicates that a particular vehicle is utilized or employed in performing an action, event, or activity.
  • C. vehicleName
    Indicates the specific name or designation assigned to a vehicle.
  • D. usedAsVehicleFor
    Indicates that one entity functions as a means of transportation or conveyance for another entity.
  • E. producedVehicle
    Indicates that one entity manufactured or created a particular vehicle.
  • F. None of above. chosen

Provenance (7 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_69e77e83c8ec8190bf52fcdac4838984 completed April 21, 2026, 1:41 p.m.
NER Named-entity recognition batch_69f5fc1385c4819082eff6432380dd2c completed May 2, 2026, 1:28 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10c170627881909c09a859450a22fe completed May 22, 2026, 8:49 p.m.
NEDg Description generation batch_6a10c2489ee48190a35add76cdf94b8b completed May 22, 2026, 8:53 p.m.
NED2 Entity disambiguation (via description) batch_6a10c30975b08190b22c052dab147afe completed May 22, 2026, 8:56 p.m.
PD Predicate disambiguation batch_69f4938262ac8190b41f922d0407d272 completed May 1, 2026, 11:50 a.m.
PDg Predicate description generation batch_69f497b8abb88190bb672cf6907c4b8d completed May 1, 2026, 12:08 p.m.
Created at: April 21, 2026, 9:08 p.m.