Triple
T13346867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prince of Schaumburg-Lippe |
E317974
|
entity |
| Predicate | territorySizeAround1900 |
P157
|
FINISHED |
| Object | about 340 square kilometres |
—
|
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: about 340 square kilometres | Statement: [Prince of Schaumburg-Lippe, territorySizeAround1900, about 340 square kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: territorySizeAround1900 Context triple: [Prince of Schaumburg-Lippe, territorySizeAround1900, about 340 square kilometres]
-
A.
territoryAtTime
Indicates that a specified territory is associated with or held by an entity during a particular time or time interval.
-
B.
hasLandAreaRange
Indicates that an entity’s land area falls within a specified minimum-to-maximum range.
-
C.
landArea
chosen
Indicates the total surface area of a piece of land associated with an entity, typically measured in standardized units (e.g., square meters, hectares).
-
D.
territoryAfter1945
Indicates the territorial status or control of an area as it existed after the year 1945.
-
E.
landAreaShare
Indicates the proportion of a larger geographic area’s total land area that is accounted for by a specific sub-area or unit.
- 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_69d806b5a3c08190b42c267fb092f98a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99e89c65c819093f3bea11d6073c5 |
completed | April 11, 2026, 1:06 a.m. |
| PD | Predicate disambiguation | batch_69d98f6e53d88190bd6aa42f69b10ffb |
completed | April 11, 2026, 12:01 a.m. |
Created at: April 9, 2026, 9:31 p.m.