Triple

T27873631
Position Surface form Disambiguated ID Type / Status
Subject GE Dash 7 series locomotives E704866 entity
Predicate locomotiveSeriesIncludes P86157 FINISHED
Object GE C36-7
The GE C36-7 is a six-axle, high-horsepower diesel-electric freight locomotive built by General Electric in the late 1970s and 1980s, widely used in heavy-haul service in North America and abroad.
 E1798254 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: GE C36-7 | Statement: [GE Dash 7 series locomotives, locomotiveSeriesIncludes, GE C36-7]
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: GE C36-7
Triple: [GE Dash 7 series locomotives, locomotiveSeriesIncludes, GE C36-7]
Generated description
The GE C36-7 is a six-axle, high-horsepower diesel-electric freight locomotive built by General Electric in the late 1970s and 1980s, widely used in heavy-haul service in North America and abroad.

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_69ef84111bb4819084298f994b31c62f completed April 27, 2026, 3:43 p.m.
NER Named-entity recognition batch_69f64cb36ed88190973a4790577762eb completed May 2, 2026, 7:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a131147cb4c8190978635b162c275c6 completed May 24, 2026, 2:55 p.m.
NEDg Description generation batch_6a1311f106748190b256e38ceb2481f2 completed May 24, 2026, 2:57 p.m.
NED2 Entity disambiguation (via description) batch_6a159f7e09a88190a7e25e30dfd87d3d completed May 26, 2026, 1:26 p.m.
Created at: April 27, 2026, 6:25 p.m.