Triple

T9732280
Position Surface form Disambiguated ID Type / Status
Subject Transilien Line L E235973 entity
Predicate usesRollingStock P5426 FINISHED
Object Z 6400
Z 6400 is a class of French electric multiple unit trains operated by SNCF for suburban commuter services around Paris.
E816344 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: Z 6400 | Statement: [Transilien Line L, usesRollingStock, Z 6400]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Z 6400
Context triple: [Transilien Line L, usesRollingStock, Z 6400]
  • A. Z 5600
    Z 5600 is a class of French electric multiple unit trains used for suburban passenger services in the Paris region.
  • B. Zen 5
    Zen 5 is AMD’s next-generation CPU microarchitecture designed to power high-performance processors across server, desktop, and mobile platforms.
  • C. SX-64
    The SX-64 is a portable, luggable version of the Commodore 64 home computer that features a built-in color monitor and floppy disk drive.
  • D. Zen 2
    Zen 2 is AMD’s second-generation Ryzen/EPYC CPU microarchitecture, known for its 7 nm process, significant IPC gains, and strong multi-core performance in desktops and servers.
  • E. R4000
    The R4000 is a 64-bit MIPS microprocessor that introduced a new generation of high-performance, RISC-based CPU designs widely used in workstations and servers in the early 1990s.
  • 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: Z 6400
Triple: [Transilien Line L, usesRollingStock, Z 6400]
Generated description
Z 6400 is a class of French electric multiple unit trains operated by SNCF for suburban commuter services around Paris.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Z 6400
Target entity description: Z 6400 is a class of French electric multiple unit trains operated by SNCF for suburban commuter services around Paris.
  • A. Z 5600
    Z 5600 is a class of French electric multiple unit trains used for suburban passenger services in the Paris region.
  • B. Zen 5
    Zen 5 is AMD’s next-generation CPU microarchitecture designed to power high-performance processors across server, desktop, and mobile platforms.
  • C. SX-64
    The SX-64 is a portable, luggable version of the Commodore 64 home computer that features a built-in color monitor and floppy disk drive.
  • D. Zen 2
    Zen 2 is AMD’s second-generation Ryzen/EPYC CPU microarchitecture, known for its 7 nm process, significant IPC gains, and strong multi-core performance in desktops and servers.
  • E. R4000
    The R4000 is a 64-bit MIPS microprocessor that introduced a new generation of high-performance, RISC-based CPU designs widely used in workstations and servers in the early 1990s.
  • 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_69ca84d313e88190983ee6ffd0ef60d2 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9eb3d6e4819090b3c7fb92550c57 completed April 1, 2026, 10:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69d19fbbba2081909a15725a68423162 completed April 4, 2026, 11:33 p.m.
NEDg Description generation batch_69d1a065ce008190985b792302daa7cb completed April 4, 2026, 11:36 p.m.
NED2 Entity disambiguation (via description) batch_69d1a0f811fc8190b6a46a0441159089 completed April 4, 2026, 11:38 p.m.
Created at: March 30, 2026, 8:22 p.m.