Triple
T5615750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sierra del Rosario |
E147472
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Soroa
Soroa is a small Cuban village and popular ecotourism destination known for its lush mountain scenery, waterfalls, and orchid garden.
|
E534760
|
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: Soroa | Statement: [Sierra del Rosario, contains, Soroa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Soroa Context triple: [Sierra del Rosario, contains, Soroa]
-
A.
Soatá
Soatá is a small town and municipality in the Boyacá Department of Colombia, known for its dry canyon landscapes and production of regional fruits.
-
B.
Somorja
Somorja is the Hungarian name for Šamorín, a town in southwestern Slovakia known for its multicultural heritage and proximity to Bratislava.
-
C.
Moura
Moura is a historic town in Portugal’s Alentejo region, known for its whitewashed architecture, olive oil production, and proximity to the Alqueva reservoir.
-
D.
Moura
Moura is a small coal-mining town in Central Queensland, Australia, known for its agricultural activities and history of mining disasters.
-
E.
Ráquira
Ráquira is a Colombian town renowned for its traditional pottery, colorful handicrafts, and vibrant colonial architecture.
- 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: Soroa Triple: [Sierra del Rosario, contains, Soroa]
Generated description
Soroa is a small Cuban village and popular ecotourism destination known for its lush mountain scenery, waterfalls, and orchid garden.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Soroa Target entity description: Soroa is a small Cuban village and popular ecotourism destination known for its lush mountain scenery, waterfalls, and orchid garden.
-
A.
Soatá
Soatá is a small town and municipality in the Boyacá Department of Colombia, known for its dry canyon landscapes and production of regional fruits.
-
B.
Somorja
Somorja is the Hungarian name for Šamorín, a town in southwestern Slovakia known for its multicultural heritage and proximity to Bratislava.
-
C.
Moura
Moura is a historic town in Portugal’s Alentejo region, known for its whitewashed architecture, olive oil production, and proximity to the Alqueva reservoir.
-
D.
Moura
Moura is a small coal-mining town in Central Queensland, Australia, known for its agricultural activities and history of mining disasters.
-
E.
Ráquira
Ráquira is a Colombian town renowned for its traditional pottery, colorful handicrafts, and vibrant colonial architecture.
- 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_69c00905d4588190bd967842bbcf2219 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c021d8d600819097df4e265e262d90 |
completed | March 22, 2026, 5:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c04d51c12c8190911fb9a0c0d234d8 |
completed | March 22, 2026, 8:13 p.m. |
| NEDg | Description generation | batch_69c04e89b7c481908abae227d22cc814 |
completed | March 22, 2026, 8:18 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c04f41b158819097f9ef536215e248 |
completed | March 22, 2026, 8:21 p.m. |
Created at: March 22, 2026, 3:39 p.m.