Triple

T30210259
Position Surface form Disambiguated ID Type / Status
Subject Nez Cassé locomotives E768049 entity
Predicate hasSubclass P1244 FINISHED
Object SNCF Class CC 6500
The SNCF Class CC 6500 is a powerful French electric locomotive series introduced in the 1960s, renowned for hauling high-speed passenger and heavy freight trains on the French rail network.
 E1911343 NE FINISHED

How this triple was built (2 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: SNCF Class CC 6500 | Statement: [Nez Cassé locomotives, hasSubclass, SNCF Class CC 6500]
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: SNCF Class CC 6500
Triple: [Nez Cassé locomotives, hasSubclass, SNCF Class CC 6500]
Generated description
The SNCF Class CC 6500 is a powerful French electric locomotive series introduced in the 1960s, renowned for hauling high-speed passenger and heavy freight trains on the French rail network.

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_69f2247eb0848190b4032f302d39c0d9 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f67ff0c0dc8190862a037439b36edf completed May 2, 2026, 10:51 p.m.
NED1 Entity disambiguation (via context triple) batch_6a277c0093508190b0451a49b4f1722d completed June 9, 2026, 2:35 a.m.
NEDg Description generation batch_6a277ec112108190a6d1f38d95e7ca26 completed June 9, 2026, 2:47 a.m.
NED2 Entity disambiguation (via description) batch_6a277f2741588190b070a8399cf110c9 completed June 9, 2026, 2:49 a.m.
Created at: April 29, 2026, 7:32 p.m.