Triple
T28793644
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mrs. Gardiner (Pride and Prejudice, 1995 TV series) |
E727025
|
entity |
| Predicate | visitsEstate |
P60586
|
FINISHED |
| Object | Pemberley (Pride and Prejudice, 1995 TV series) |
—
|
NE NERFINISHED |
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: Pemberley (Pride and Prejudice, 1995 TV series) | Statement: [Mrs. Gardiner (Pride and Prejudice, 1995 TV series), visitsEstate, Pemberley (Pride and Prejudice, 1995 TV series)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: visitsEstate Context triple: [Mrs. Gardiner (Pride and Prejudice, 1995 TV series), visitsEstate, Pemberley (Pride and Prejudice, 1995 TV series)]
-
A.
visits
chosen
Indicates that one entity goes to or spends time at the location or presence of another entity.
-
B.
countryEstate
Indicates that a particular estate or property is located within or belongs to a specified country.
-
C.
nobleEstate
Indicates that an entity is a noble’s estate or property associated with nobility.
-
D.
oneOfFourEstates
Indicates that an entity belongs to one specific member of a defined group of four estates or categories.
-
E.
countryHouseOnEstate
Indicates that a country house is located on, and forms part of, a particular estate.
- F. None of above.
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_69f0319b7c44819085736bcc256185e6 |
completed | April 28, 2026, 4:03 a.m. |
| NER | Named-entity recognition | batch_69f658ee40088190b71e1219407690d0 |
completed | May 2, 2026, 8:05 p.m. |
| PD | Predicate disambiguation | batch_69f65760fd3081908ffe014a5e2bf069 |
completed | May 2, 2026, 7:58 p.m. |
Created at: April 28, 2026, 6:24 a.m.