Triple
T38490432
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montgomery City Lines |
E918038
|
entity |
| Predicate | transportedPassengerGroup |
P20804
|
FINISHED |
| Object | white residents of Montgomery |
—
|
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: white residents of Montgomery | Statement: [Montgomery City Lines, transportedPassengerGroup, white residents of Montgomery]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: transportedPassengerGroup Context triple: [Montgomery City Lines, transportedPassengerGroup, white residents of Montgomery]
-
A.
transportedGroup
chosen
Indicates that one entity moved or carried a group of entities from one location to another.
-
B.
primaryPassengerGroup
Indicates the main group of passengers that is most directly associated with or served by a given entity or context.
-
C.
passengersType
Indicates the type or category of passengers associated with or involved in a given entity or context.
-
D.
hasThroughPassengersWith
Indicates that two transportation segments, services, or locations are connected by passengers who travel through them without starting or ending their journey there.
-
E.
passengers
Indicates that one entity is traveling in or being transported by another entity, typically as a non-operating occupant.
- 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_69f76e9894208190a129a553a60ca58c |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a000014497c819088d5cda3977522dd |
completed | May 10, 2026, 3:48 a.m. |
| PD | Predicate disambiguation | batch_69ffff9a52b08190be1024e0fb6fe661 |
completed | May 10, 2026, 3:46 a.m. |
Created at: May 3, 2026, 4:31 p.m.