Triple
T19302461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arlington Row |
E482735
|
entity |
| Predicate | convertedToCottagesDate |
P10480
|
FINISHED |
| Object | 17th century |
—
|
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: 17th century | Statement: [Arlington Row, convertedToCottagesDate, 17th century]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: convertedToCottagesDate Context triple: [Arlington Row, convertedToCottagesDate, 17th century]
-
A.
dateOfConversion
chosen
Indicates the specific date on which an entity changed status, form, or affiliation (e.g., religious, legal, or organizational conversion).
-
B.
convertedToHotel
Indicates that something previously used for another purpose has been transformed and repurposed into a hotel.
-
C.
hasNumberOfCottages
Indicates the quantity of cottages associated with a given entity.
-
D.
renovatedAfter
Indicates that one entity was renovated at a later time than another entity.
-
E.
convertedIn
Indicates that one entity has been transformed, changed, or translated into another form, state, or representation within a specified context.
- 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_69d8e8d04d5c8190baa816986f2b1d1e |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fc8add788190aed98bcbad518808 |
completed | April 20, 2026, 10:14 a.m. |
| PD | Predicate disambiguation | batch_69e4dd0bc7508190a6f9d56bd4c3404f |
completed | April 19, 2026, 1:47 p.m. |
Created at: April 10, 2026, 1:31 p.m.