Triple

T27491071
Position Surface form Disambiguated ID Type / Status
Subject Elmwood Depot E693884 entity
Predicate servesRoute P31354 FINISHED
Object SEPTA Route 101
SEPTA Route 101 is a suburban trolley line in the Philadelphia area that runs between the 69th Street Transportation Center and Media, Pennsylvania.
 E641149 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: SEPTA Route 101 | Statement: [Elmwood Depot, servesRoute, SEPTA Route 101]
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: SEPTA Route 101
Triple: [Elmwood Depot, servesRoute, SEPTA Route 101]
Generated description
SEPTA Route 101 is a suburban trolley line in the Philadelphia area that runs between the 69th Street Transportation Center and Media, Pennsylvania.

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_69ef5382b9648190be0b1ef2ad5d043c completed April 27, 2026, 12:16 p.m.
NER Named-entity recognition batch_69f62e8941e081908e2b8e35bd5461cb completed May 2, 2026, 5:04 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12da74c3b88190909e394cb28767db completed May 24, 2026, 11:01 a.m.
NEDg Description generation batch_6a12db2833688190af921e97c6e5d05d completed May 24, 2026, 11:04 a.m.
NED2 Entity disambiguation (via description) batch_6a12db977df48190b71bce8408b51269 completed May 24, 2026, 11:05 a.m.
Created at: April 27, 2026, 1:05 p.m.