Triple
T28526313
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MBTA bus route 8X |
E721914
|
entity |
| Predicate | hadStopPattern |
P102636
|
FINISHED |
| Object | limited number of stops |
—
|
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: limited number of stops | Statement: [MBTA bus route 8X, hadStopPattern, limited number of stops]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadStopPattern Context triple: [MBTA bus route 8X, hadStopPattern, limited number of stops]
-
A.
stopsPattern
Indicates that one entity halts, interrupts, or prevents the continuation of a recurring or structured pattern involving another entity.
-
B.
hasStop
Indicates that something (such as a route, service, or journey) includes or is associated with a particular stop or stopping point.
-
C.
typicalStopPattern
chosen
Indicates the usual or most common sequence or arrangement of stops associated with an entity’s operation or route.
-
D.
hasStopFeature
Indicates that one entity possesses or is equipped with a feature that enables stopping or halting an associated process, action, or movement.
-
E.
stoppedBy
Indicates that one entity causes another entity to cease moving, operating, or continuing an action.
- 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_69f01a5d7ec88190ada2d5be7c06c35d |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_69f65876c52c8190bc889c7a67bd07f3 |
completed | May 2, 2026, 8:03 p.m. |
| PD | Predicate disambiguation | batch_69f6575d89788190aca478e4aea05a65 |
completed | May 2, 2026, 7:58 p.m. |
Created at: April 28, 2026, 3:25 a.m.