Triple

T24963260
Position Surface form Disambiguated ID Type / Status
Subject Silver Line SL3 E624666 entity
Predicate isPartOfCorridor P35 FINISHED
Object MBTA Silver Line corridor
The MBTA Silver Line corridor is a bus rapid transit network in Boston that connects key neighborhoods and Logan Airport with frequent, limited-stop service using dedicated lanes and tunnel segments.
E1690075 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: MBTA Silver Line corridor | Statement: [Silver Line SL3, isPartOfCorridor, MBTA Silver Line corridor]
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: MBTA Silver Line corridor
Triple: [Silver Line SL3, isPartOfCorridor, MBTA Silver Line corridor]
Generated description
The MBTA Silver Line corridor is a bus rapid transit network in Boston that connects key neighborhoods and Logan Airport with frequent, limited-stop service using dedicated lanes and tunnel segments.

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_69e2ff23a3a88190b1b9743fe5e15f94 completed April 18, 2026, 3:48 a.m.
NER Named-entity recognition batch_69f4242f0968819098c3f0f92054d22f completed May 1, 2026, 3:55 a.m.
NED1 Entity disambiguation (via context triple) batch_6a10c10c67b48190acedb8c3eb1ae208 completed May 22, 2026, 8:48 p.m.
NEDg Description generation batch_6a10c1db351c819082d9d7ff8c9f130b completed May 22, 2026, 8:51 p.m.
NED2 Entity disambiguation (via description) batch_6a10c2897e348190b1fa494f5891d742 completed May 22, 2026, 8:54 p.m.
Created at: April 18, 2026, 5:59 a.m.