Triple
T8795016
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Companhia Caminho Aéreo Pão de Açúcar |
E209267
|
entity |
| Predicate | hasPrimaryCustomerType |
P809
|
FINISHED |
| Object | tourists |
—
|
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: tourists | Statement: [Companhia Caminho Aéreo Pão de Açúcar, hasPrimaryCustomerType, tourists]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPrimaryCustomerType Context triple: [Companhia Caminho Aéreo Pão de Açúcar, hasPrimaryCustomerType, tourists]
-
A.
customerType
chosen
Indicates the classification or category assigned to a customer based on their characteristics, status, or relationship with a business.
-
B.
hasBusinessTypeAlong
Indicates that a business or commercial entity located along a route, corridor, or area is associated with a specific type or category of business activity.
-
C.
primaryUserType
Indicates the main or dominant category of user associated with an entity or interaction.
-
D.
hasCustomerIndustry
Indicates that a customer is associated with or operates within a particular industry or sector.
-
E.
hasPrimaryTrafficType
Indicates that an entity is associated with a main or predominant type of traffic it handles or is designed for.
- 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_69ca836240888190a62b262e56a69d2f |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5fa0c6008190a5c4d87510ad5bbd |
completed | March 31, 2026, 11:58 p.m. |
| PD | Predicate disambiguation | batch_69cc5c1d48f08190b325a77d4c76d223 |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:43 p.m.