Triple
T3609019
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beira Litoral |
E76438
|
entity |
| Predicate | includesCity |
P3207
|
FINISHED |
| Object | Anadia |
E374159
|
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: Anadia | Statement: [Beira Litoral, includesCity, Anadia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anadia Context triple: [Beira Litoral, includesCity, Anadia]
-
A.
Anadia
chosen
Anadia is a municipality and town in Portugal known for its wine production and thermal spas, located in the country's Centro Region.
-
B.
Kanosh
Kanosh is a small town in central Utah known for its rural setting and historical ties to the early Mormon settlement of Millard County.
-
C.
Minlaton
Minlaton is a rural service town on South Australia's Yorke Peninsula, known for its agricultural production and historic aviation connections.
-
D.
Anapa
Anapa is a resort city on Russia’s Black Sea coast, known for its sandy beaches, mild climate, and popularity as a family vacation destination.
-
E.
Ochre City
Ochre City is a popular nickname for Marrakesh, referring to the Moroccan city's distinctive red and ochre-colored buildings and walls.
- 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_69ad85da0ba481908b3b48c69efe2b98 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc22a3cf081908c20b6fb55be0db2 |
completed | March 8, 2026, 6:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b44f02b3c081909705e05ac923f840 |
completed | March 13, 2026, 5:53 p.m. |
Created at: March 8, 2026, 3:22 p.m.