Triple
T10277584
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harling Road |
E241007
|
entity |
| Predicate | hasStationStyle |
P93228
|
FINISHED |
| Object | rural wayside station |
—
|
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: rural wayside station | Statement: [Harling Road, hasStationStyle, rural wayside station]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStationStyle Context triple: [Harling Road, hasStationStyle, rural wayside station]
-
A.
hasStationIcon
Indicates that an entity is associated with a specific icon used to visually represent a station.
-
B.
hasStationStructure
Indicates that an entity possesses or is associated with a particular station-related physical structure.
-
C.
hasStationFunction
Indicates that an entity serves in a particular functional role or capacity at a station.
-
D.
hasParkStyle
Indicates that an entity possesses or is characterized by a particular style or type of park.
-
E.
hasDedicatedStations
Indicates that specific stations are exclusively assigned or reserved for a particular entity or purpose.
- 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_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. |
| PDg | Predicate description generation | batch_69d4d7cada7881908beba55a1dc9ecb9 |
completed | April 7, 2026, 10:09 a.m. |
Created at: April 6, 2026, 11:37 a.m.