Triple
T841815
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Quincy, Florida |
E18193
|
entity |
| Predicate | hasHistoricEconomy |
P10778
|
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: [Quincy, Florida, hasHistoricEconomy, agriculture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHistoricEconomy Context triple: [Quincy, Florida, hasHistoricEconomy, agriculture]
-
A.
historicalEconomicBase
chosen
Indicates the primary type of economic activity or production that historically underpinned or sustained an entity’s economy.
-
B.
hasEconomicDevelopment
Indicates that one entity possesses, experiences, or is characterized by a certain level or type of economic growth, progress, or improvement in its economic conditions.
-
C.
hasHistoricIndustry
Indicates that an entity has been associated with a notable or historically significant industry or industrial activity in the past.
-
D.
hasHistoricalEntity
Indicates a relationship where one entity includes, references, or is associated with another entity that existed or is defined in a past historical context.
-
E.
hasHistoricalContext
Indicates that something is related to, influenced by, or best understood in light of specific past events, conditions, or time periods.
- 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_69a49389f44881909a608fb27d89f247 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4abe6f0dc8190a1bebb5e21f4ceac |
completed | March 1, 2026, 9:13 p.m. |
| PD | Predicate disambiguation | batch_69a4aa7dfc5c8190890c9df485d73a86 |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:38 p.m.