Triple
T2340245
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Land Ordinance of 1785 |
E45008
|
entity |
| Predicate | definedSectionsPerTownship |
P34935
|
FINISHED |
| Object | 36 |
—
|
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 | Statement: [Land Ordinance of 1785, definedSectionsPerTownship, 36]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: definedSectionsPerTownship Context triple: [Land Ordinance of 1785, definedSectionsPerTownship, 36]
-
A.
sectionsPerTownship
chosen
Indicates the number of land sections that are contained within a single township.
-
B.
hasMunicipalSections
Indicates that a municipality is divided into and associated with specific internal administrative sections or districts.
-
C.
numberOfDistricts
Indicates the total count of districts associated with a given entity or area.
-
D.
divisionTitleCount
Indicates the number of titles or championships associated with a particular division.
-
E.
hasNumberOfMunicipalities
Indicates the relationship that specifies how many municipalities are associated with or contained within a given administrative or geographic 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.