Triple
T10279496
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red Line (St. Louis MetroLink) |
E241057
|
entity |
| Predicate | servesKeyDestinations |
P30653
|
FINISHED |
| Object | airport |
—
|
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: airport | Statement: [Red Line (St. Louis MetroLink), servesKeyDestinations, airport]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servesKeyDestinations Context triple: [Red Line (St. Louis MetroLink), servesKeyDestinations, airport]
-
A.
servedDestination
Indicates that a service or action is directed toward, or provided to, a particular destination.
-
B.
servesDestinationType
chosen
Indicates that an entity provides service to, or is intended for use with, a specific type or category of destination.
-
C.
servesDestinationCount
Indicates the number of distinct destinations that an entity (such as a service, route, or provider) serves.
-
D.
servesAsGatewayTo
Indicates that one entity functions as an entry point, access route, or intermediary channel that enables reaching or connecting to another entity.
-
E.
servedByDirection
Indicates that something (such as a route, service, or facility) is provided or accessed in a specific directional orientation (e.g., northbound, inbound).
- 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_69d381a94c1881908fc38fc263d9b9c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d7ccb7ec8190a538cf279e48116e |
completed | April 7, 2026, 10:09 a.m. |
| PD | Predicate disambiguation | batch_69d4d1f117708190928f92ae2611d724 |
completed | April 7, 2026, 9:44 a.m. |
Created at: April 6, 2026, 11:38 a.m.