Triple
T25848889
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wilmington and Western Railroad |
E651145
|
entity |
| Predicate | typicalPassengerMarket |
P27830
|
FINISHED |
| Object | tourists |
—
|
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: tourists | Statement: [Wilmington and Western Railroad, typicalPassengerMarket, tourists]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalPassengerMarket Context triple: [Wilmington and Western Railroad, typicalPassengerMarket, tourists]
-
A.
airlineMarket
Indicates a commercial air transport relationship where an airline provides or operates flight services within a specific origin–destination market or route.
-
B.
servesPassengerTrafficType
chosen
Indicates that a transportation facility or service accommodates a specified type or category of passenger traffic.
-
C.
servedPassengerTraffic
Indicates that an entity has provided transportation services to a certain volume or set of passengers.
-
D.
passengerFlight
Indicates a relationship where a flight is specifically operated to transport passengers rather than cargo or other purposes.
-
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_69e7ab39035c8190be15c8aaee1bb858 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69fdd5fba5048190b7d430ae2054a1fd |
completed | May 8, 2026, 12:24 p.m. |
| PD | Predicate disambiguation | batch_69fdd35f76f88190a1854ea27132f9c7 |
completed | May 8, 2026, 12:13 p.m. |
Created at: April 22, 2026, 7:57 a.m.