Triple
T32587675
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Campo Maior |
E832970
|
entity |
| Predicate | hasCoffeeTradition |
P82187
|
FINISHED |
| Object | longstanding coffee roasting tradition |
—
|
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: longstanding coffee roasting tradition | Statement: [Campo Maior, hasCoffeeTradition, longstanding coffee roasting tradition]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCoffeeTradition Context triple: [Campo Maior, hasCoffeeTradition, longstanding coffee roasting tradition]
-
A.
hasCoffeeCulture
chosen
Indicates that a place or group is characterized by established social practices, traditions, and norms centered around the consumption and appreciation of coffee.
-
B.
coffeeHeritage
Indicates a historical or cultural connection between entities and the tradition, origin, or legacy of coffee.
-
C.
countryInTradition
Indicates that a country participates in, is associated with, or belongs to a particular cultural, historical, or religious tradition.
-
D.
coffeeIntroducedBy
Indicates that a particular coffee was brought, presented, or made known to someone by a specific person or source.
-
E.
hasCaffeineContent
Indicates that one entity (typically a beverage or substance) possesses a specified amount or presence of caffeine.
- 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_69f34929ff648190aded9424aa7564ae |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a037cad051c8190b28b354b89208574 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a0379f0cbe481909b4b8fc6cbe297f0 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 1:04 a.m.