Triple

T27679380
Position Surface form Disambiguated ID Type / Status
Subject Credit Mobilier scandal E697867 entity
Predicate involves P1256 FINISHED
Object Credit Mobilier of America
Credit Mobilier of America was a 19th-century American railroad construction company notorious for its central role in a major political corruption scandal involving Union Pacific Railroad and U.S. congressmen.
 E1784918 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: Credit Mobilier of America | Statement: [Credit Mobilier scandal, involves, Credit Mobilier of America]
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: Credit Mobilier of America
Triple: [Credit Mobilier scandal, involves, Credit Mobilier of America]
Generated description
Credit Mobilier of America was a 19th-century American railroad construction company notorious for its central role in a major political corruption scandal involving Union Pacific Railroad and U.S. congressmen.

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_69ef590d458c81909583290c3cd0478b completed April 27, 2026, 12:39 p.m.
NER Named-entity recognition batch_69f6353654188190915266b42fb1885a completed May 2, 2026, 5:32 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12daabf61081908ce11ec2c6816deb completed May 24, 2026, 11:02 a.m.
NEDg Description generation batch_6a12de8086248190bb53e2b9d526e5e8 completed May 24, 2026, 11:18 a.m.
NED2 Entity disambiguation (via description) batch_6a12dee961508190bf952dc985a4e377 completed May 24, 2026, 11:20 a.m.
Created at: April 27, 2026, 2:45 p.m.