Triple
T19682236
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Metro Orange Line |
E472621
|
entity |
| Predicate | partOfNetwork |
P840
|
FINISHED |
| Object | Metrorail |
—
|
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: Metrorail | Statement: [Metro Orange Line, partOfNetwork, Metrorail]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Metrorail Context triple: [Metro Orange Line, partOfNetwork, Metrorail]
-
A.
Metrorail
Metrorail is Miami-Dade County’s elevated rapid transit system that connects key neighborhoods, suburbs, and downtown Miami.
-
B.
Metrorail
chosen
Metrorail is the rapid transit system serving the Washington, D.C. metropolitan area, operated by the Washington Metropolitan Area Transit Authority (WMATA).
-
C.
Metrorail
Metrorail is a South African commuter rail service that operates urban and suburban passenger trains in major metropolitan areas across the country.
-
D.
Metro Rail
Metro Rail is the light rail rapid transit system serving the Buffalo–Niagara region of New York.
-
E.
Metro Rail
Metro Rail is the urban rapid transit rail system serving Los Angeles County, providing light rail and subway services across the region.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e514f2e08190ba70a4449519d218 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e641c05964819093d3124b8f174001 |
completed | April 20, 2026, 3:09 p.m. |
Created at: April 10, 2026, 1:45 p.m.