Triple
T7131047
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Physiocracy |
E166186
|
entity |
| Predicate | viewsSectorAsProductive |
P75029
|
FINISHED |
| Object | agriculture |
—
|
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: agriculture | Statement: [Physiocracy, viewsSectorAsProductive, agriculture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: viewsSectorAsProductive Context triple: [Physiocracy, viewsSectorAsProductive, agriculture]
-
A.
ownerSector
Indicates the sector or industry category to which the owner of an entity belongs.
-
B.
notableSector
Indicates that an entity is particularly prominent, influential, or significant within a specified sector or industry.
-
C.
economicSectorSourceOfWealth
Indicates that a particular economic sector is the primary source from which an entity derives its wealth or income.
-
D.
hasIndustrialSector
Indicates that an entity is associated with, operates in, or belongs to a particular industrial sector or branch of economic activity.
-
E.
associatedWithEconomicSector
Indicates that an entity has a connection or involvement with a particular economic sector, such as operating, participating, or being relevant within that sector.
- F. None of above. chosen
Provenance (4 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_69c68884a9388190af42f90d1c1a7151 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e66dc2388190bdec018f1cc6b20a |
completed | March 27, 2026, 8:19 p.m. |
| PD | Predicate disambiguation | batch_69c6e1c7289881909f3b533c384f9ed4 |
completed | March 27, 2026, 8 p.m. |
| PDg | Predicate description generation | batch_69c6e4a213508190a40aca39f9eee7d5 |
completed | March 27, 2026, 8:12 p.m. |
Created at: March 27, 2026, 2:44 p.m.