Triple
T2882586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Burghley House park |
E59429
|
entity |
| Predicate | nearBorderWith |
P224
|
FINISHED |
| Object | Rutland |
E86840
|
NE 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: Rutland | Statement: [Burghley House park, nearBorderWith, Rutland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rutland Context triple: [Burghley House park, nearBorderWith, Rutland]
-
A.
Rutland
chosen
Rutland is a small historic county in the East Midlands of England, known for its rural character and Rutland Water reservoir.
-
B.
Rutland
Rutland is an unincorporated community located in Bibb County, Georgia, United States.
-
C.
Rutland
Rutland is a small city in central Vermont known historically as a marble quarrying center and as a regional hub for commerce and outdoor recreation.
-
D.
Rutland
Rutland is a small town in Worcester County, Massachusetts, known for its rural character and location near the geographic center of the state.
-
E.
Berkshire
Berkshire is a historic county in South East England known for its royal connections, including Windsor Castle, and its mix of affluent towns and rural landscapes.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ab4ac739188190a112f42a5a69c951 |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abe02c238881908f7a349563c388bf |
completed | March 7, 2026, 8:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b03167d7dc819093e91e0d42f3de6f |
completed | March 10, 2026, 2:57 p.m. |
Created at: March 6, 2026, 10:03 p.m.