Triple

T30778309
Position Surface form Disambiguated ID Type / Status
Subject Glendale, Queens E783737 entity
Predicate servedByBus P14525 FINISHED
Object Q39 bus
The Q39 bus is a New York City Transit route that connects several neighborhoods in Queens, including Glendale, with other parts of the borough.
 E1930611 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: Q39 bus | Statement: [Glendale, Queens, servedByBus, Q39 bus]
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: Q39 bus
Triple: [Glendale, Queens, servedByBus, Q39 bus]
Generated description
The Q39 bus is a New York City Transit route that connects several neighborhoods in Queens, including Glendale, with other parts of the borough.

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_69f224b213c8819083886073f90b647e completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f68fe2a3ec8190a75f3d2f21ff14f7 completed May 2, 2026, 11:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a28b0a010408190bac3b0e5a7d706bb completed June 10, 2026, 12:32 a.m.
NEDg Description generation batch_6a28b1ecd9448190a88b0f465f97a8ba completed June 10, 2026, 12:38 a.m.
NED2 Entity disambiguation (via description) batch_6a28b28e61f881908ea11ba5951789bd completed June 10, 2026, 12:40 a.m.
Created at: April 29, 2026, 8:41 p.m.