Triple
T2340244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Land Ordinance of 1785 |
E45008
|
entity |
| Predicate | definedTownshipArea |
P14731
|
FINISHED |
| Object | 36 square miles |
—
|
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: 36 square miles | Statement: [Land Ordinance of 1785, definedTownshipArea, 36 square miles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: definedTownshipArea Context triple: [Land Ordinance of 1785, definedTownshipArea, 36 square miles]
-
A.
representedArea
Indicates that one entity serves as a representation or depiction of a particular area or region.
-
B.
areaApprox
Indicates that one entity’s area is approximately equal to the area of another entity.
-
C.
territorialDesignation
chosen
Indicates that an entity is assigned, defined, or recognized in terms of a specific geographic or territorial area.
-
D.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
E.
hasNumberOfAcres
Indicates the specific quantity of land area, measured in acres, that is associated with an entity.
- 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_69a88917935081909b755dbf38e81024 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abc6f75d888190a2e41edaa532e83f |
completed | March 7, 2026, 6:34 a.m. |
| PD | Predicate disambiguation | batch_69abc594087c819098100a10c5478a4b |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 4, 2026, 7:52 p.m.