Triple
T8617683
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rockport Harbor |
E204081
|
entity |
| Predicate | partOfLocalEconomySector |
P47145
|
FINISHED |
| Object | tourism |
—
|
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: tourism | Statement: [Rockport Harbor, partOfLocalEconomySector, tourism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: partOfLocalEconomySector Context triple: [Rockport Harbor, partOfLocalEconomySector, tourism]
-
A.
localEconomyImpact
Indicates the effect that an action, event, or entity has on the economic conditions, activities, or performance of a specific local area or community.
-
B.
economicSectorSourceOfWealth
Indicates that a particular economic sector is the primary source from which an entity derives its wealth or income.
-
C.
regionalEconomyType
Indicates the type or classification of an economy associated with a specific region.
-
D.
associatedWithEconomicSector
chosen
Indicates that an entity has a connection or involvement with a particular economic sector, such as operating, participating, or being relevant within that sector.
-
E.
ownerSector
Indicates the sector or industry category to which the owner of an entity belongs.
- 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_69ca832ceab8819096e4a9f546695079 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc4711c7748190af26ff5a78ef66a2 |
completed | March 31, 2026, 10:13 p.m. |
| PD | Predicate disambiguation | batch_69cc455437488190b7506f820daf6e32 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:26 p.m.