Triple
T20030188
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MARTA heavy rail cars |
E495099
|
entity |
| Predicate | belongsToTransitMode |
P28705
|
FINISHED |
| Object | heavy rail |
—
|
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: heavy rail | Statement: [MARTA heavy rail cars, belongsToTransitMode, heavy rail]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: belongsToTransitMode Context triple: [MARTA heavy rail cars, belongsToTransitMode, heavy rail]
-
A.
associatedWithTransitSystem
Indicates that an entity has a connection or involvement with a particular transit or transportation system.
-
B.
associatedWithTransitLine
Indicates that an entity has a connection or linkage to a specific transit line, such as being part of, served by, or otherwise related to that line.
-
C.
appliesToTransportMode
Indicates that a rule, condition, or characteristic is specifically associated with and relevant to a particular mode of transport.
-
D.
publicTransitMode
chosen
Indicates the type of public transportation (e.g., bus, train, subway) used or associated with a given trip or segment.
-
E.
appliesToTransitSystem
Indicates that something (such as a rule, policy, feature, or condition) is relevant or applicable to a particular transit or transportation system.
- 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_69da626bfd288190aa5d65098b6433ae |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66291a00c8190b0b895909f32d623 |
completed | April 20, 2026, 5:29 p.m. |
| PD | Predicate disambiguation | batch_69e54ce752748190a0a1ffddd0372271 |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 11, 2026, 3:36 p.m.