Triple
T4239984
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mission, Kansas |
E95387
|
entity |
| Predicate | hasLandAreaCharacteristic |
P12436
|
FINISHED |
| Object | small land area |
—
|
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: small land area | Statement: [Mission, Kansas, hasLandAreaCharacteristic, small land area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLandAreaCharacteristic Context triple: [Mission, Kansas, hasLandAreaCharacteristic, small land area]
-
A.
hasLandAreaSquareKilometers
Indicates that an entity possesses a land area measured in square kilometers.
-
B.
landArea
Indicates the total surface area of a piece of land associated with an entity, typically measured in standardized units (e.g., square meters, hectares).
-
C.
hasLargestContinuousLandAreaOn
Indicates that an entity possesses the greatest uninterrupted expanse of land on a specified geographic region or surface compared to all other entities.
-
D.
hasGeographyCharacteristic
chosen
Indicates that an entity possesses a specific geographical feature, property, or attribute.
-
E.
hasLandCoverage
Indicates that a specified area or region is covered or occupied by a particular type of land surface or land use.
- 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_69b3453d91548190b4d4ef8fe52aa2ac |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b34e77fb5c8190b298818acb68ff63 |
completed | March 12, 2026, 11:38 p.m. |
| PD | Predicate disambiguation | batch_69b347f587148190a1830503459939b6 |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:05 p.m.