Triple
T24060225
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bounds Green |
E595926
|
entity |
| Predicate | hasRailLinkNearby |
P25143
|
FINISHED |
| Object |
Bowes Park railway station
Bowes Park railway station is a suburban rail station in North London that serves local commuter services on the Hertford Loop Line between central London and Hertfordshire.
|
E1618549
|
NE FINISHED |
How this triple was built (3 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: Bowes Park railway station | Statement: [Bounds Green, hasRailLinkNearby, Bowes Park railway station]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Bowes Park railway station Triple: [Bounds Green, hasRailLinkNearby, Bowes Park railway station]
Generated description
Bowes Park railway station is a suburban rail station in North London that serves local commuter services on the Hertford Loop Line between central London and Hertfordshire.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRailLinkNearby Context triple: [Bounds Green, hasRailLinkNearby, Bowes Park railway station]
-
A.
hasNearbyRailway
chosen
Indicates that one entity is located close to a railway associated with or relevant to another entity.
-
B.
hasRailStation
Indicates that one entity possesses, contains, or is served by a rail station.
-
C.
hasMajorRailLinksTo
Indicates that there are significant railway connections or routes between two locations.
-
D.
hasRailRoute
Indicates that there exists a rail-based transportation route or connection between the related entities.
-
E.
connectsToRailStation
Indicates that one entity has a direct link, route, or access connection to a rail station.
- F. None of above.
Provenance (6 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_69e288c25c008190850cf447940ab181 |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1da55903c8190ad5d578e33a9dae9 |
completed | April 29, 2026, 10:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0f96601c6481908a3e035005025201 |
completed | May 21, 2026, 11:33 p.m. |
| NEDg | Description generation | batch_6a0f9a8d66948190a339664b2b85f1d5 |
completed | May 21, 2026, 11:51 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0f9b0e3e588190bcbbdfea80ee54f6 |
completed | May 21, 2026, 11:53 p.m. |
| PD | Predicate disambiguation | batch_69f1764b1d4c8190b12590c6339c31c1 |
completed | April 29, 2026, 3:08 a.m. |
Created at: April 17, 2026, 10:38 p.m.