Triple

T31867768
Position Surface form Disambiguated ID Type / Status
Subject Alstom E813505 entity
Predicate notableProduct P1448 FINISHED
Object AGV high-speed train
The AGV high-speed train is a next-generation, articulated electric multiple unit known for its very high speeds, energy efficiency, and advanced passenger comfort features.
 E1980719 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: AGV high-speed train | Statement: [Alstom, notableProduct, AGV high-speed train]
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: AGV high-speed train
Triple: [Alstom, notableProduct, AGV high-speed train]
Generated description
The AGV high-speed train is a next-generation, articulated electric multiple unit known for its very high speeds, energy efficiency, and advanced passenger comfort features.

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_69f348ecb07481909c8f72619131b115 completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6b094d7b881908c82c8a54665d20f completed May 3, 2026, 2:19 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2e65bf0c608190af99c129817b129d completed June 14, 2026, 8:26 a.m.
NEDg Description generation batch_6a2e682ae8e08190bbdfbd08104a11b9 completed June 14, 2026, 8:36 a.m.
NED2 Entity disambiguation (via description) batch_6a2e6b3d1ea08190a6e48fbf597d0782 completed June 14, 2026, 8:50 a.m.
Created at: April 30, 2026, 11:54 p.m.