Triple

T29990055
Position Surface form Disambiguated ID Type / Status
Subject West 25th–Ohio City E761848 entity
Predicate servedByLine P1293 FINISHED
Object Red Line
The Red Line is a rapid transit service of Cleveland's RTA system that runs east–west through the city, connecting key neighborhoods and destinations including Ohio City.
 E760813 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: Red Line | Statement: [West 25th–Ohio City, servedByLine, Red Line]
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: Red Line
Triple: [West 25th–Ohio City, servedByLine, Red Line]
Generated description
The Red Line is a rapid transit service of Cleveland's RTA system that runs east–west through the city, connecting key neighborhoods and destinations including Ohio City.

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_69f224695498819094a81037cad401e2 completed April 29, 2026, 3:31 p.m.
NER Named-entity recognition batch_69f6791985c08190815809bcc3258be8 completed May 2, 2026, 10:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a274c9215b88190bac427fb3457202e completed June 8, 2026, 11:13 p.m.
NEDg Description generation batch_6a275089c4a48190a19421c7bc5fdc1f completed June 8, 2026, 11:30 p.m.
NED2 Entity disambiguation (via description) batch_6a27515ddfd88190be08dfcf4659a411 completed June 8, 2026, 11:33 p.m.
Created at: April 29, 2026, 6:38 p.m.