Triple
T1566902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mark VI monorail trains |
E33451
|
entity |
| Predicate | passengerServiceStatus |
P26411
|
FINISHED |
| Object | in service |
—
|
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: in service | Statement: [Mark VI monorail trains, passengerServiceStatus, in service]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: passengerServiceStatus Context triple: [Mark VI monorail trains, passengerServiceStatus, in service]
-
A.
hasPassengerServicesTo
Indicates that a transportation provider operates passenger services connecting one location or entity to another.
-
B.
formerPassengerService
Indicates that an entity previously provided passenger transportation services but no longer does so.
-
C.
hasOccupancyStatus
chosen
Indicates the current usage or availability state of something, such as whether it is occupied, vacant, or otherwise in use.
-
D.
hasPassengerRole
Indicates that an entity participates in a context or event specifically in the capacity or role of a passenger.
-
E.
passengerCount
Indicates the number of passengers associated with a given entity, such as a vehicle or trip.
- 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_69a885f11b048190935025a035302715 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90fccd4b48190a44012888a00af7f |
completed | March 5, 2026, 5:08 a.m. |
| PD | Predicate disambiguation | batch_69a907b872f0819096b3df6ad502c63e |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:27 p.m.