Triple
T28304739
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gipsy Hill railway station |
E713807
|
entity |
| Predicate | connectsAreaTo |
P97914
|
FINISHED |
| Object | central London |
—
|
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: central London | Statement: [Gipsy Hill railway station, connectsAreaTo, central London]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: connectsAreaTo Context triple: [Gipsy Hill railway station, connectsAreaTo, central London]
-
A.
connectsArea
Indicates that one area serves as a link or passage between two other areas, enabling movement or interaction between them.
-
B.
connectsCentralAreaTo
chosen
Indicates a relationship where one element serves as a link or pathway between a central area and another location or component.
-
C.
connectsAreaNear
Indicates that one entity serves as a link or passage between areas that are geographically close to each other.
-
D.
hasAreaConnections
Indicates that an entity is linked to one or more surrounding or related areas, typically representing spatial or regional connections between them.
-
E.
connectsToAreaKnownFor
Indicates that something has a direct link or association to a specific area that is recognized or notable for certain characteristics or features.
- 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_69efb524ab688190a1ce7ee7c9520932 |
completed | April 27, 2026, 7:12 p.m. |
| NER | Named-entity recognition | batch_69fd485f57dc8190820365396d041991 |
completed | May 8, 2026, 2:20 a.m. |
| PD | Predicate disambiguation | batch_69fd47d35da081908bec8901018d186c |
completed | May 8, 2026, 2:17 a.m. |
Created at: April 27, 2026, 11:37 p.m.