Triple

T26821072
Position Surface form Disambiguated ID Type / Status
Subject Key System E675246 entity
Predicate networkName P22978 FINISHED
Object Key Route System
Key Route System was a major early 20th-century electric streetcar and interurban rail network serving the San Francisco Bay Area, particularly the East Bay.
 E1742265 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: Key Route System | Statement: [Key System, networkName, Key Route System]
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: Key Route System
Triple: [Key System, networkName, Key Route System]
Generated description
Key Route System was a major early 20th-century electric streetcar and interurban rail network serving the San Francisco Bay Area, particularly the East Bay.

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_69eee9b6b28481909332f83eb17e5170 completed April 27, 2026, 4:44 a.m.
NER Named-entity recognition batch_69f61a89d73c8190b1287cab1ce69805 completed May 2, 2026, 3:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12097ada648190bf45371b8cdc261b completed May 23, 2026, 8:09 p.m.
NEDg Description generation batch_6a120a3f4550819095c30c79f5faa104 completed May 23, 2026, 8:12 p.m.
NED2 Entity disambiguation (via description) batch_6a120b0591e0819080d57a6f01e4128b completed May 23, 2026, 8:16 p.m.
Created at: April 27, 2026, 4:55 a.m.