Triple

T6925396
Position Surface form Disambiguated ID Type / Status
Subject Egelsbach E160292 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object DA
DA is the vehicle registration code for the German city of Darmstadt and its surrounding district in the state of Hesse.
 E629812 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: DA | Statement: [Egelsbach, vehicleRegistrationCode, DA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: DA
Context triple: [Egelsbach, vehicleRegistrationCode, DA]
  • A. DA
    DA is the official abbreviation for the United States Department of the Army, the federal agency responsible for organizing, training, and equipping the U.S. Army.
  • B. DA
    DA is a postcode area in southeast England covering parts of south-east London and northwest Kent, including towns such as Dartford and Sidcup.
  • C. DA
    DA is the commonly used abbreviation for the Defence Academy of the United Kingdom, the institution responsible for advanced education and training of the UK’s armed forces and defence personnel.
  • D. DAR
    DAR is the Philippine government agency responsible for implementing agrarian reform and redistributing agricultural land to farmers.
  • E. DAL
    DAL is the standard three-letter abbreviation used for the NHL team the Dallas Stars.
  • 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: DA
Triple: [Egelsbach, vehicleRegistrationCode, DA]
Generated description
DA is the vehicle registration code for the German city of Darmstadt and its surrounding district in the state of Hesse.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: DA
Target entity description: DA is the vehicle registration code for the German city of Darmstadt and its surrounding district in the state of Hesse.
  • A. DA
    DA is the official abbreviation for the United States Department of the Army, the federal agency responsible for organizing, training, and equipping the U.S. Army.
  • B. DA
    DA is a postcode area in southeast England covering parts of south-east London and northwest Kent, including towns such as Dartford and Sidcup.
  • C. DA
    DA is the commonly used abbreviation for the Defence Academy of the United Kingdom, the institution responsible for advanced education and training of the UK’s armed forces and defence personnel.
  • D. DAR
    DAR is the Philippine government agency responsible for implementing agrarian reform and redistributing agricultural land to farmers.
  • E. DAL
    DAL is the standard three-letter abbreviation used for the NHL team the Dallas Stars.
  • 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_69c6884d350081908d8a970e4d40ad78 completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6da18b6388190947dfc1eb9e5d382 completed March 27, 2026, 7:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7513fddd88190b99c4b7e3364d218 completed March 28, 2026, 3:55 a.m.
NEDg Description generation batch_69c7523737e48190b2ad3e7bea02878d completed March 28, 2026, 3:59 a.m.
NED2 Entity disambiguation (via description) batch_69c752eb3e1c8190bad35727e573c41f completed March 28, 2026, 4:02 a.m.
Created at: March 27, 2026, 2:26 p.m.