Triple
T10294578
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coastal Region of Ecuador |
E241449
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object |
Costa
Costa is the low-lying, tropical coastal region of Ecuador known for its beaches, port cities, and agricultural production.
|
E855936
|
NE FINISHED |
How this triple was built (4 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: Costa | Statement: [Coastal Region of Ecuador, alsoKnownAs, Costa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Costa Context triple: [Coastal Region of Ecuador, alsoKnownAs, Costa]
-
A.
Costa
Costa is a common Portuguese surname borne by numerous notable figures in politics, sports, and the arts.
-
B.
Costa
Costa is a Chilean wine-producing subregion within the Cachapoal Valley, known for its coastal influence that shapes the style and character of its wines.
-
C.
Costa Nova
Costa Nova is a picturesque seaside village in Portugal famed for its colorful striped houses and sandy Atlantic beach near Aveiro.
-
D.
Costa Cálida
Costa Cálida is a popular coastal region in southeastern Spain known for its warm climate, sandy beaches, and seaside resorts along the Mediterranean.
-
E.
Costa Esmeralda
Costa Esmeralda is a scenic coastal tourist region in eastern Mexico known for its long stretches of sandy beaches, warm Gulf waters, and relaxed resort atmosphere.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Costa Triple: [Coastal Region of Ecuador, alsoKnownAs, Costa]
Generated description
Costa is the low-lying, tropical coastal region of Ecuador known for its beaches, port cities, and agricultural production.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Costa Target entity description: Costa is the low-lying, tropical coastal region of Ecuador known for its beaches, port cities, and agricultural production.
-
A.
Costa
Costa is a common Portuguese surname borne by numerous notable figures in politics, sports, and the arts.
-
B.
Costa
Costa is a Chilean wine-producing subregion within the Cachapoal Valley, known for its coastal influence that shapes the style and character of its wines.
-
C.
Costa Nova
Costa Nova is a picturesque seaside village in Portugal famed for its colorful striped houses and sandy Atlantic beach near Aveiro.
-
D.
Costa Cálida
Costa Cálida is a popular coastal region in southeastern Spain known for its warm climate, sandy beaches, and seaside resorts along the Mediterranean.
-
E.
Costa Esmeralda
Costa Esmeralda is a scenic coastal tourist region in eastern Mexico known for its long stretches of sandy beaches, warm Gulf waters, and relaxed resort atmosphere.
- F. None of above. chosen
Provenance (5 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_69d381aaafc08190af475ef58dc16aba |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2d5e0f88190be3e23ba2511a1e9 |
completed | April 7, 2026, 9:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d71d23f49081909aea149c6b219354 |
completed | April 9, 2026, 3:29 a.m. |
| NEDg | Description generation | batch_69d73182d7548190ac15093aa7001db7 |
completed | April 9, 2026, 4:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d7336c06308190ac72154134a26842 |
completed | April 9, 2026, 5:04 a.m. |
Created at: April 6, 2026, 11:42 a.m.