Triple
T1596159
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MBTA bus route 1 |
E34286
|
entity |
| Predicate | ridershipLevel |
P29684
|
FINISHED |
| Object | heavily used |
—
|
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: heavily used | Statement: [MBTA bus route 1, ridershipLevel, heavily used]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ridershipLevel Context triple: [MBTA bus route 1, ridershipLevel, heavily used]
-
A.
primaryRiders
Indicates that the referenced entities are the main or principal riders associated with a particular vehicle, trip, or ride-related event.
-
B.
numberOfRidersPerVehicle
Indicates the quantity of riders associated with each individual vehicle in the relationship.
-
C.
teamLevel
Indicates the hierarchical rank or tier at which a team operates within an organization, competition, or structure.
-
D.
honorLevel
Indicates the degree or status of respect, distinction, or recognition accorded to an entity relative to others.
-
E.
trainingLevel
Indicates the degree or stage of training or skill development that an entity has attained.
- F. None of above. chosen
Provenance (4 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_69a885fdcb9c819081ce6f0b8cd477dd |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a916d413f08190a4e137e5ed262e25 |
completed | March 5, 2026, 5:38 a.m. |
| PD | Predicate disambiguation | batch_69a907bfb39c8190a31e0be14d3d52e6 |
completed | March 5, 2026, 4:34 a.m. |
| PDg | Predicate description generation | batch_69a916d2fae48190aaac6b2a5e31a7cf |
completed | March 5, 2026, 5:38 a.m. |
Created at: March 4, 2026, 7:27 p.m.