Triple
T43654
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ERP |
E858
|
entity |
| Predicate | beneficiaryCountry |
P3061
|
FINISHED |
| Object | Portugal |
E866
|
NE 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: Portugal | Statement: [ERP, beneficiaryCountry, Portugal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Portugal Context triple: [ERP, beneficiaryCountry, Portugal]
-
A.
Portugal
chosen
Portugal is a Southern European country on the Iberian Peninsula, known for its maritime history, Atlantic coastline, and role as one of the world’s earliest global colonial powers.
-
B.
Spain
Spain is a southwestern European country on the Iberian Peninsula, historically a major global colonial power and now a constitutional monarchy and member of the European Union.
-
C.
Algarve
Algarve is a popular coastal region in southern Portugal known for its beaches, cliffs, and resort towns.
-
D.
Azores
The Azores are a remote Portuguese archipelago in the North Atlantic Ocean, known for their volcanic landscapes, lush greenery, and mild maritime climate.
-
E.
Lisbon
Lisbon is the coastal capital city of Portugal, renowned for its historic architecture, hilly landscape, and role as a major cultural and economic center in Europe.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24ec1ef5481909daf99654dfa3f57 |
completed | Feb. 28, 2026, 2:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2aa40b9fc819092323d52f6ae489d |
completed | Feb. 28, 2026, 8:41 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.