Triple
T30137575
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metrorail Orange Line |
E766037
|
entity |
| Predicate | connectsDowntown |
P26184
|
FINISHED |
| Object | Washington, D.C. |
—
|
NE NERFINISHED |
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: Washington, D.C. | Statement: [Metrorail Orange Line, connectsDowntown, Washington, D.C.]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: connectsDowntown Context triple: [Metrorail Orange Line, connectsDowntown, Washington, D.C.]
-
A.
connectsDowntownTo
chosen
Indicates a relationship where one location, route, or service provides a direct connection or access to a downtown area.
-
B.
connectsCity
Indicates a relationship where one entity serves as a link or route that joins or provides direct access between two cities.
-
C.
connectsCityTo
Indicates a relationship in which a route, infrastructure, or link joins one city to another, enabling connection or interaction between them.
-
D.
isDowntownEndpointOf
Indicates that a location serves as the downtown terminus or endpoint of a route, line, or path.
-
E.
connectsCentralDistrictsWith
Indicates a relationship where something serves as a link or route joining central districts to one another.
- F. None of above.
Provenance (3 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_69f2247909048190ae86c2160cf8b566 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fdee770af48190aca2670db50f8b49 |
completed | May 8, 2026, 2:08 p.m. |
| PD | Predicate disambiguation | batch_69fdecec98a08190a357d816dc2a6dbe |
completed | May 8, 2026, 2:02 p.m. |
Created at: April 29, 2026, 7:16 p.m.