Triple
T41722
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brandeis/Roberts |
E823
|
entity |
| Predicate | hasTicketing |
P3383
|
FINISHED |
| Object | onboard or mobile ticketing |
—
|
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: onboard or mobile ticketing | Statement: [Brandeis/Roberts, hasTicketing, onboard or mobile ticketing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTicketing Context triple: [Brandeis/Roberts, hasTicketing, onboard or mobile ticketing]
-
A.
hasFaregates
Indicates that an entity is equipped with or contains faregates used to control or validate access, typically for paid entry.
-
B.
fareControl
Indicates that an entity is responsible for monitoring, enforcing, or managing payment of fares for access to a service or facility.
-
C.
hasPassengerTerminal
Indicates that one entity possesses or is equipped with a passenger terminal used for boarding, alighting, or handling passengers.
-
D.
hasReception
Indicates that an entity hosts, includes, or is associated with a reception event (such as a formal gathering or welcoming function).
-
E.
hasFareZone
Indicates that an entity is located within or associated with a specific fare zone used for pricing or ticketing.
- 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24db9527c8190816b6b25c88cb2f4 |
completed | Feb. 28, 2026, 2:06 a.m. |
| PD | Predicate disambiguation | batch_69a24ab8a8908190beec6da6694dd4c9 |
completed | Feb. 28, 2026, 1:54 a.m. |
| PDg | Predicate description generation | batch_69a24db81c748190948560892f12c61b |
completed | Feb. 28, 2026, 2:06 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.