Triple
T16841580
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Borchester |
E409424
|
entity |
| Predicate | hasFictionalTransportLink |
P125068
|
FINISHED |
| Object | roads to Ambridge |
—
|
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: roads to Ambridge | Statement: [Borchester, hasFictionalTransportLink, roads to Ambridge]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalTransportLink Context triple: [Borchester, hasFictionalTransportLink, roads to Ambridge]
-
A.
hasFictionalTubeStation
Indicates that an entity features or is associated with a tube (subway) station that exists only in fiction rather than in reality.
-
B.
hasFictionalUndergroundStation
Indicates that an entity features or includes a subway/metro station that exists only in fiction rather than in the real world.
-
C.
hasTramway
Indicates that a location or area is served by, contains, or is connected to a tramway system.
-
D.
hasBridgeOrFerryConnection
Indicates that there exists a bridge or ferry link enabling direct passage or transport between the related entities.
-
E.
hasFictionalLandmark
Indicates that one entity includes, features, or is associated with a landmark that is fictional rather than real.
- 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_69d883952b048190887740a980b712ed |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b35167a48190b45a459023e3ab1b |
completed | April 18, 2026, 4:37 p.m. |
| PD | Predicate disambiguation | batch_69e32b87b4248190aaddb05e88452356 |
completed | April 18, 2026, 6:58 a.m. |
| PDg | Predicate description generation | batch_69e34fb7c8c8819086975b7955b7d8ef |
completed | April 18, 2026, 9:32 a.m. |
Created at: April 10, 2026, 5:24 a.m.