Triple

T24563507
Position Surface form Disambiguated ID Type / Status
Subject Brookland–CUA station E607719 entity
Predicate locatedNear P294 FINISHED
Object Monroe Street NE
Monroe Street NE is a major east–west thoroughfare in Washington, D.C.’s Brookland neighborhood, known for connecting residential areas, local businesses, and the nearby Catholic University of America campus.
 E1644415 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: Monroe Street NE | Statement: [Brookland–CUA station, locatedNear, Monroe Street NE]
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: Monroe Street NE
Triple: [Brookland–CUA station, locatedNear, Monroe Street NE]
Generated description
Monroe Street NE is a major east–west thoroughfare in Washington, D.C.’s Brookland neighborhood, known for connecting residential areas, local businesses, and the nearby Catholic University of America campus.

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_69e2c4cc35a48190990b7571bc086df8 completed April 17, 2026, 11:39 p.m.
NER Named-entity recognition batch_69f2a8f8a7488190a11f32014539a4a3 completed April 30, 2026, 12:57 a.m.
NED1 Entity disambiguation (via context triple) batch_6a100472823081909f7eead2c32ba3e1 completed May 22, 2026, 7:23 a.m.
NEDg Description generation batch_6a1005ee1150819092f6b15e13cc9258 completed May 22, 2026, 7:29 a.m.
NED2 Entity disambiguation (via description) batch_6a100726903c81908e4d72caeed62502 completed May 22, 2026, 7:35 a.m.
Created at: April 18, 2026, 2:28 a.m.