Triple
T6542058
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Northwest–East Line |
E168311
|
entity |
| Predicate | connectsPartOfCity |
P70030
|
FINISHED |
| Object | northwest Baltimore |
—
|
LITERAL 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: northwest Baltimore | Statement: [Northwest–East Line, connectsPartOfCity, northwest Baltimore]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: connectsPartOfCity Context triple: [Northwest–East Line, connectsPartOfCity, northwest Baltimore]
-
A.
connectsCityTo
Indicates a relationship in which a route, infrastructure, or link joins one city to another, enabling connection or interaction between them.
-
B.
connectsCity
Indicates a relationship where one entity serves as a link or route that joins or provides direct access between two cities.
-
C.
connectsCityIndirectly
Indicates that one location is linked to a city through one or more intermediate locations or routes, rather than by a direct connection.
-
D.
connectsPartOf
chosen
Indicates a relationship where one entity serves to link or join a component to the larger whole of which that component is a part.
-
E.
connectsDowntownTo
Indicates a relationship where one location, route, or service provides a direct connection or access to a downtown area.
- 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_69c68a51564081909e93aee0dbd9cca3 |
completed | March 27, 2026, 1:46 p.m. |
| NER | Named-entity recognition | batch_69c6ce07332481909a5a7964282eb776 |
completed | March 27, 2026, 6:35 p.m. |
| PD | Predicate disambiguation | batch_69c6acf3e3708190b052ec774e607cb7 |
completed | March 27, 2026, 4:14 p.m. |
Created at: March 27, 2026, 1:50 p.m.