Triple
T2340249
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Land Ordinance of 1785 |
E45008
|
entity |
| Predicate | reservedSectionNumber |
P8879
|
FINISHED |
| Object | section 16 of each township |
—
|
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: section 16 of each township | Statement: [Land Ordinance of 1785, reservedSectionNumber, section 16 of each township]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: reservedSectionNumber Context triple: [Land Ordinance of 1785, reservedSectionNumber, section 16 of each township]
-
A.
isSectionNumber
chosen
Indicates that one entity is the section number identifier associated with another entity, typically within a structured document or text.
-
B.
sectionAddressed
Indicates that a specific section or part of a document, text, or resource is being referred to, targeted, or dealt with by an action or statement.
-
C.
section
Indicates that one entity is a distinct part, division, or segment of another entity within a larger whole.
-
D.
standardSectionArea
Indicates that one entity specifies the standard or nominal cross-sectional area associated with another entity.
-
E.
sectionType
Indicates the specific kind or category of section that an entity belongs to or represents.
- 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.