Triple

T1924771
Position Surface form Disambiguated ID Type / Status
Subject Münster E40803 entity
Predicate vehicleRegistrationCode P1173 FINISHED
Object MS
MS is the official vehicle registration code used on license plates for the German city of Münster.
 E215539 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: MS | Statement: [Münster, vehicleRegistrationCode, MS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MS
Context triple: [Münster, vehicleRegistrationCode, MS]
  • A. MS
    MS is the two-letter ISO 3166 country code assigned to the British Overseas Territory of Montserrat in the Caribbean.
  • B. MS
    MS is the official two-letter United States Postal Service abbreviation for the state of Mississippi.
  • C. MSA
    MSA is the common abbreviation for the Master Settlement Agreement, a landmark 1998 legal settlement between major U.S. tobacco companies and state attorneys general that reshaped tobacco advertising and funded public health initiatives.
  • D. MSA
    MSA is the standardized, literary form of Arabic used in formal writing, media, education, and official communication across the Arab world.
  • E. MSA
    MSA is the commonly used abbreviation for the Magnuson–Stevens Fishery Conservation and Management Act, the primary law governing marine fisheries management in U.S. federal waters.
  • 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: MS
Triple: [Münster, vehicleRegistrationCode, MS]
Generated description
MS is the official vehicle registration code used on license plates for the German city of Münster.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MS
Target entity description: MS is the official vehicle registration code used on license plates for the German city of Münster.
  • A. MS
    MS is the official two-letter United States Postal Service abbreviation for the state of Mississippi.
  • B. MS
    MS is the two-letter ISO 3166 country code assigned to the British Overseas Territory of Montserrat in the Caribbean.
  • C. MSA
    MSA is the common abbreviation for the Master Settlement Agreement, a landmark 1998 legal settlement between major U.S. tobacco companies and state attorneys general that reshaped tobacco advertising and funded public health initiatives.
  • D. MSA
    MSA is the standardized, literary form of Arabic used in formal writing, media, education, and official communication across the Arab world.
  • E. MSA
    MSA is the commonly used abbreviation for the Magnuson–Stevens Fishery Conservation and Management Act, the primary law governing marine fisheries management in U.S. federal waters.
  • 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_69a8864711648190b07bed24ed76258e completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb260da088190ac53bfc9437e112b completed March 7, 2026, 5:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69adf3e881748190b00f125185e91271 completed March 8, 2026, 10:10 p.m.
NEDg Description generation batch_69adf494f0288190bf77285d18fcd3d9 completed March 8, 2026, 10:13 p.m.
NED2 Entity disambiguation (via description) batch_69adf51f49488190b9b0465b4da685c1 completed March 8, 2026, 10:15 p.m.
Created at: March 4, 2026, 7:35 p.m.