Triple
T16976481
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liberia, Costa Rica |
E411825
|
entity |
| Predicate | hasNearbyTouristDestination |
P3449
|
FINISHED |
| Object |
Tamarindo
Tamarindo is a popular beach town on Costa Rica’s Pacific coast known for its surfing, nightlife, and laid-back tourist atmosphere.
|
E1243589
|
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: Tamarindo | Statement: [Liberia, Costa Rica, hasNearbyTouristDestination, Tamarindo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tamarindo Context triple: [Liberia, Costa Rica, hasNearbyTouristDestination, Tamarindo]
-
A.
Tamarindo
Tamarindo is a neighborhood within Havana’s Diez de Octubre municipality, known as a densely populated urban residential area in Cuba’s capital.
-
B.
San Blas
San Blas is the Spanish name for Saint Blaise, a Christian bishop and martyr venerated as a patron saint of throat ailments.
-
C.
San Blas
San Blas is a coastal town and port in the Mexican state of Nayarit, known for its beaches, fishing, and nearby mangrove and bird-filled wetlands.
-
D.
San Blas
San Blas is a Madrid Metro station serving the San Blas-Canillejas district in the east of Madrid, Spain.
-
E.
San Blas
San Blas is a small settlement in Cuba’s Ciénaga de Zapata region, known for its proximity to extensive wetlands and rich biodiversity.
- 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: Tamarindo Triple: [Liberia, Costa Rica, hasNearbyTouristDestination, Tamarindo]
Generated description
Tamarindo is a popular beach town on Costa Rica’s Pacific coast known for its surfing, nightlife, and laid-back tourist atmosphere.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tamarindo Target entity description: Tamarindo is a popular beach town on Costa Rica’s Pacific coast known for its surfing, nightlife, and laid-back tourist atmosphere.
-
A.
Tamarindo
Tamarindo is a neighborhood within Havana’s Diez de Octubre municipality, known as a densely populated urban residential area in Cuba’s capital.
-
B.
San Blas
San Blas is the Spanish name for Saint Blaise, a Christian bishop and martyr venerated as a patron saint of throat ailments.
-
C.
San Blas
San Blas is a Madrid Metro station serving the San Blas-Canillejas district in the east of Madrid, Spain.
-
D.
San Blas
San Blas is a small settlement in Cuba’s Ciénaga de Zapata region, known for its proximity to extensive wetlands and rich biodiversity.
-
E.
San Blas
San Blas is a coastal town and port in the Mexican state of Nayarit, known for its beaches, fishing, and nearby mangrove and bird-filled wetlands.
- 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_69d886ca8f348190812768ea8d5055ce |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d183ff648190a3cb47242bf7e6ba |
completed | April 18, 2026, 6:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00d4755ffc8190a5c861462e33d526 |
completed | May 10, 2026, 6:54 p.m. |
| NEDg | Description generation | batch_6a00d687aed88190915e20e8fa517a2b |
completed | May 10, 2026, 7:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00d6ecbf60819095e3f8418a84d485 |
completed | May 10, 2026, 7:05 p.m. |
Created at: April 10, 2026, 5:32 a.m.