Triple
T7331447
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Washington Square (Brookline) |
E169008
|
entity |
| Predicate | primaryTransitMode |
P70707
|
FINISHED |
| Object | light 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: light rail | Statement: [Washington Square (Brookline), primaryTransitMode, light rail]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryTransitMode Context triple: [Washington Square (Brookline), primaryTransitMode, light rail]
-
A.
publicTransitMode
Indicates the type of public transportation (e.g., bus, train, subway) used or associated with a given trip or segment.
-
B.
transportModeImportant
Indicates that the mode of transport used is considered significant or plays an important role in the context of the relationship or action.
-
C.
hasPrimaryTransportationMode
chosen
Indicates the main or most frequently used mode of transportation associated with an entity.
-
D.
primaryTransportModel
Indicates that one transport model is designated as the main or default model used for a given context or entity.
-
E.
passesUsedForTransportation
Indicates that the passes are utilized as a means or instrument for transporting people or goods.
- 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_69c68a568a6481908f11e20db7bc8446 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f347f25081908e6086d4073295f5 |
completed | March 27, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69c6f028fd748190b2ea5c3081958a42 |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:03 p.m.