Triple
T13965941
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hamlyn Terrace |
E335921
|
entity |
| Predicate | distanceToNewcastle_km |
P111976
|
FINISHED |
| Object | approximately 60 |
—
|
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: approximately 60 | Statement: [Hamlyn Terrace, distanceToNewcastle_km, approximately 60]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToNewcastle_km Context triple: [Hamlyn Terrace, distanceToNewcastle_km, approximately 60]
-
A.
distanceFromNewcastle
Indicates the spatial distance between a given entity or location and the city of Newcastle.
-
B.
distanceToSunderland_km
Indicates the physical distance, measured in kilometers, between a given place and Sunderland.
-
C.
distanceFromSunderland
Indicates the spatial distance between a given entity and the location of Sunderland.
-
D.
distanceToEdinburgh_km
Indicates the physical distance, measured in kilometers, between an entity’s location and Edinburgh.
-
E.
distanceFromDurhamCity
Indicates the measured distance between an entity’s location and the city of Durham.
- 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_69d81c61f3508190aaf2ca0dc0002c59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e8c9e988190a84c9ca8a78b515f |
completed | April 14, 2026, 12:09 p.m. |
| PD | Predicate disambiguation | batch_69dd465a21408190b912a42c50ffa0d9 |
completed | April 13, 2026, 7:39 p.m. |
| PDg | Predicate description generation | batch_69de01ed2098819088ec45069f6f2609 |
completed | April 14, 2026, 8:59 a.m. |
Created at: April 9, 2026, 10:18 p.m.