Triple
T32818609
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red Line (Washington Metro) stations |
E839370
|
entity |
| Predicate | serveCounty |
P79950
|
FINISHED |
| Object | Montgomery County, Maryland |
—
|
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: Montgomery County, Maryland | Statement: [Red Line (Washington Metro) stations, serveCounty, Montgomery County, Maryland]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: serveCounty Context triple: [Red Line (Washington Metro) stations, serveCounty, Montgomery County, Maryland]
-
A.
inCounty
Indicates that one entity is geographically or administratively located within the boundaries of a specified county.
-
B.
servesCountySeat
Indicates that one entity functions as the county seat (administrative center) for the specified county.
-
C.
servesCountyTown
chosen
Indicates that an entity (such as a service, office, or facility) provides coverage or service to a specified county or town.
-
D.
startCounty
Indicates the county in which something (such as an event, route, or process) begins or originates.
-
E.
includesCounty
Indicates that a larger geographic or administrative region contains or encompasses a specific county within its boundaries.
- 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_69f3493df9008190a8f5d843dcd77704 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69ff409ff5548190849c2d50e99bd807 |
completed | May 9, 2026, 2:11 p.m. |
| PD | Predicate disambiguation | batch_69ff401a5e188190a72f945e910b4a6c |
completed | May 9, 2026, 2:09 p.m. |
Created at: May 1, 2026, 1:15 a.m.