Triple

T7407300
Position Surface form Disambiguated ID Type / Status
Subject Ludwigshafen am Rhein E170906 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object LU
LU is the vehicle registration code for the German city of Ludwigshafen am Rhein in the state of Rhineland-Palatinate.
 E662420 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: LU | Statement: [Ludwigshafen am Rhein, vehicleRegistrationCode, LU]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LU
Context triple: [Ludwigshafen am Rhein, vehicleRegistrationCode, LU]
  • A. LU
    LU is the two-letter ISO 3166-1 alpha-2 country code assigned to Luxembourg for international identification and data standards.
  • B. LU
    LU is the commonly used abbreviation for the University of Latvia, a major public research university in Riga.
  • C. Lu
    Lu is the traditional abbreviation and historical name used to refer to China’s Shandong province.
  • D. LLU
    LLU is the vehicle registration code assigned to the town of Kock in Poland.
  • E. UL
    UL is the New York Stock Exchange ticker symbol for Unilever, a major multinational consumer goods company known for its food, personal care, and household products.
  • 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: LU
Triple: [Ludwigshafen am Rhein, vehicleRegistrationCode, LU]
Generated description
LU is the vehicle registration code for the German city of Ludwigshafen am Rhein in the state of Rhineland-Palatinate.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LU
Target entity description: LU is the vehicle registration code for the German city of Ludwigshafen am Rhein in the state of Rhineland-Palatinate.
  • A. LU
    LU is the two-letter ISO 3166-1 alpha-2 country code assigned to Luxembourg for international identification and data standards.
  • B. LU
    LU is the commonly used abbreviation for the University of Latvia, a major public research university in Riga.
  • C. Lu
    Lu is the traditional abbreviation and historical name used to refer to China’s Shandong province.
  • D. LLU
    LLU is the vehicle registration code assigned to the town of Kock in Poland.
  • E. UL
    UL is the New York Stock Exchange ticker symbol for Unilever, a major multinational consumer goods company known for its food, personal care, and household products.
  • 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_69c68a6010108190925e5284de022660 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f298f2388190afc944c9bc78749a completed March 27, 2026, 9:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8111adbf48190a04df3cec1017b39 completed March 28, 2026, 5:34 p.m.
NEDg Description generation batch_69c81455967c81909757f42d976b535c completed March 28, 2026, 5:48 p.m.
NED2 Entity disambiguation (via description) batch_69c814b413a08190b7e5e85d73ddb430 completed March 28, 2026, 5:49 p.m.
Created at: March 27, 2026, 3:10 p.m.