Triple
T27390323
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sharptown, Maryland |
E691504
|
entity |
| Predicate | regionEconomyHistoricallyBasedOn |
P10778
|
FINISHED |
| Object | maritime trade |
—
|
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: maritime trade | Statement: [Sharptown, Maryland, regionEconomyHistoricallyBasedOn, maritime trade]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionEconomyHistoricallyBasedOn Context triple: [Sharptown, Maryland, regionEconomyHistoricallyBasedOn, maritime trade]
-
A.
historicalEconomicBase
chosen
Indicates the primary type of economic activity or production that historically underpinned or sustained an entity’s economy.
-
B.
regionalEconomyType
Indicates the type or classification of an economy associated with a specific region.
-
C.
economicArea
Indicates that one entity is part of, associated with, or falls under the jurisdiction of a defined economic region or zone of another entity.
-
D.
regionalEconomicImportance
Indicates the degree to which something significantly contributes to or influences the economy of a specific geographic region.
-
E.
economicImpactRegion
Indicates the region or geographic area that experiences or is affected by a particular economic impact.
- 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_69ef520386788190bc92cfcd97ebb67a |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69f7b2f3a104819098ddd8909eaf596c |
completed | May 3, 2026, 8:41 p.m. |
| PD | Predicate disambiguation | batch_69f7b1b8a9fc8190a1279e67a2d12707 |
completed | May 3, 2026, 8:36 p.m. |
Created at: April 27, 2026, 12:25 p.m.