Triple
T6211680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hyde Park Railroad Station |
E138883
|
entity |
| Predicate | servedNearbyEstates |
P68933
|
FINISHED |
| Object | Hudson River estates in Hyde Park area |
—
|
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: Hudson River estates in Hyde Park area | Statement: [Hyde Park Railroad Station, servedNearbyEstates, Hudson River estates in Hyde Park area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servedNearbyEstates Context triple: [Hyde Park Railroad Station, servedNearbyEstates, Hudson River estates in Hyde Park area]
-
A.
nearbyState
Indicates that one state is geographically adjacent to or in close proximity to another state.
-
B.
servesSuburbsOf
Indicates that a service, route, or facility provides coverage or support to the suburban areas associated with a particular city or region.
-
C.
residesNear
Indicates that one entity lives or is located in close physical proximity to another entity.
-
D.
nearbyUSBase
Indicates that one entity is geographically close to a United States military base.
-
E.
meetsNear
Indicates that two entities meet or come together at a location that is in close proximity to a specified reference point or area.
- 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_69c008ada364819096c9e92c74d639b5 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0628adccc8190b94f5c2c1d5d03f7 |
completed | March 22, 2026, 9:43 p.m. |
| PD | Predicate disambiguation | batch_69c055fdea3c81908f5d910f0d36234a |
completed | March 22, 2026, 8:50 p.m. |
| PDg | Predicate description generation | batch_69c056c965ac8190b938502fa8c74e1b |
completed | March 22, 2026, 8:53 p.m. |
Created at: March 22, 2026, 4:21 p.m.