Triple
T22760871
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Groby Park |
E562982
|
entity |
| Predicate | countryEstateType |
P149626
|
FINISHED |
| Object | rural estate |
—
|
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: rural estate | Statement: [Groby Park, countryEstateType, rural estate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countryEstateType Context triple: [Groby Park, countryEstateType, rural estate]
-
A.
countryEstate
Indicates that a particular estate or property is located within or belongs to a specified country.
-
B.
countryEstateOf
Indicates that one entity is the rural or countryside property owned, controlled, or associated with another entity.
-
C.
countryHouseOnEstate
Indicates that a country house is located on, and forms part of, a particular estate.
-
D.
countryHouseType
Indicates the type or category of house associated with or characteristic of a particular country.
-
E.
realEstateCategory
Indicates the classification of a property into a specific type or category within real estate (e.g., residential, commercial, industrial).
- 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_69e24552e11c81909c2d61578a558bd7 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17a7c45b881908b29ba1439038789 |
completed | April 29, 2026, 3:26 a.m. |
| PD | Predicate disambiguation | batch_69eed2b88d88819096015deb6a648801 |
completed | April 27, 2026, 3:06 a.m. |
| PDg | Predicate description generation | batch_69eeeb5681f88190821129ced752f190 |
completed | April 27, 2026, 4:51 a.m. |
Created at: April 17, 2026, 3:26 p.m.