Triple

T2978343
Position Surface form Disambiguated ID Type / Status
Subject Mercedes-Benz 600 Pullman E80450 entity
Predicate series P1761 FINISHED
Object W100
W100 is the internal Mercedes-Benz chassis code for the ultra-luxury 600 limousine series produced in the 1960s and 1970s.
 E317172 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: W100 | Statement: [Mercedes-Benz 600 Pullman, series, W100]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: W100
Context triple: [Mercedes-Benz 600 Pullman, series, W100]
  • A. J100
    J100 is the internal model code used by Lexus to designate the second generation of its full-size luxury SUV, the Lexus LX.
  • B. SP100
    SP100 is the ticker symbol used by data vendors to represent the S&P 100 stock market index, which tracks 100 major blue-chip U.S. companies.
  • C. O-10
    O-10 is the highest pay grade for four-star flag and general officers in the U.S. Armed Forces, including admirals and full generals.
  • D. W8
    W8 is a central London postcode district covering the affluent Kensington area, known for its upscale residences, shops, and cultural institutions.
  • E. WPN
    WPN is the vehicle registration code assigned to the town of Płońsk in Poland.
  • 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: W100
Triple: [Mercedes-Benz 600 Pullman, series, W100]
Generated description
W100 is the internal Mercedes-Benz chassis code for the ultra-luxury 600 limousine series produced in the 1960s and 1970s.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: W100
Target entity description: W100 is the internal Mercedes-Benz chassis code for the ultra-luxury 600 limousine series produced in the 1960s and 1970s.
  • A. J100
    J100 is the internal model code used by Lexus to designate the second generation of its full-size luxury SUV, the Lexus LX.
  • B. SP100
    SP100 is the ticker symbol used by data vendors to represent the S&P 100 stock market index, which tracks 100 major blue-chip U.S. companies.
  • C. O-10
    O-10 is the highest pay grade for four-star flag and general officers in the U.S. Armed Forces, including admirals and full generals.
  • D. W8
    W8 is a central London postcode district covering the affluent Kensington area, known for its upscale residences, shops, and cultural institutions.
  • E. WPN
    WPN is the vehicle registration code assigned to the town of Płońsk in Poland.
  • 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_69ad8b15f6ac8190be5fd16a33edcb4f completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad999b0d50819093dac7678b887a9b completed March 8, 2026, 3:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69b108ef607c8190865b079beb1b6da5 completed March 11, 2026, 6:17 a.m.
NEDg Description generation batch_69b10bc71c708190b1e620d41278c3e0 completed March 11, 2026, 6:29 a.m.
NED2 Entity disambiguation (via description) batch_69b10c43a7c48190b63a7b3f0f180d44 completed March 11, 2026, 6:31 a.m.
Created at: March 8, 2026, 2:58 p.m.