Triple
T2844978
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nazca Desert |
E62561
|
entity |
| Predicate | primaryEconomicActivitiesNearby |
P1099
|
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: [Nazca Desert, primaryEconomicActivitiesNearby, tourism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryEconomicActivitiesNearby Context triple: [Nazca Desert, primaryEconomicActivitiesNearby, 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.
hasEconomicActivity
chosen
Indicates that an entity engages in, supports, or is associated with a specific type of economic activity or business operation.
-
C.
hasNearbyIndustry
Indicates that an entity is located close to one or more industrial facilities or activities.
-
D.
nearbyUrbanCenter
Indicates that one location is geographically close to an urban center, such as a city or large town.
-
E.
regionalEconomyType
Indicates the type or classification of an economy associated with a specific region.
- 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_69ab4c3d16bc81908b3a1c98fbd287fe |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdf1b58c88190b45d8c5a76dc52ac |
completed | March 7, 2026, 8:17 a.m. |
| PD | Predicate disambiguation | batch_69abdd0e86808190bcefffafbd3cd441 |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 10:02 p.m.