Triple
T6809644
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New London Union Station |
E156596
|
entity |
| Predicate | stationCodeSystem |
P57418
|
FINISHED |
| Object | Amtrak station code |
—
|
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: Amtrak station code | Statement: [New London Union Station, stationCodeSystem, Amtrak station code]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: stationCodeSystem Context triple: [New London Union Station, stationCodeSystem, Amtrak station code]
-
A.
hasStationCodeSystem
chosen
Indicates that an entity uses or is associated with a particular system for assigning or managing station codes.
-
B.
hasStationCode
Indicates that an entity is associated with a specific station identification code.
-
C.
stationNumber
Indicates the specific station identifier or code assigned to an entity within a system or network.
-
D.
stationType
Indicates the specific category or classification of a station based on its function, services, or operational characteristics.
-
E.
stationName
Indicates the name assigned to a particular station in the relationship.
- 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_69c68828b26c819090fe9df7612bbc27 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d30c741881909e220b05aa564bc2 |
completed | March 27, 2026, 6:57 p.m. |
| PD | Predicate disambiguation | batch_69c6d099bf08819089a9f9894d037e74 |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:16 p.m.