Triple
T10668055
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sila Mountains |
E251408
|
entity |
| Predicate | hasLake |
P1025
|
FINISHED |
| Object |
Lago Cecita
Lago Cecita is an artificial mountain lake and major reservoir located in the Sila plateau of Calabria, southern Italy.
|
E877571
|
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: Lago Cecita | Statement: [Sila Mountains, hasLake, Lago Cecita]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lago Cecita Context triple: [Sila Mountains, hasLake, Lago Cecita]
-
A.
Lago Toro
Lago Toro is a scenic mountain lake located within Chile’s Huerquehue National Park, known for its forested surroundings and hiking access.
-
B.
Gutiérrez Lake
Gutiérrez Lake is a glacial Andean lake in Argentina’s Patagonia region, known for its clear waters, surrounding forests, and outdoor recreation opportunities near Bariloche.
-
C.
Pilarcitos Lake
Pilarcitos Lake is a man-made reservoir on the San Francisco Peninsula that stores water as part of the region’s municipal supply system.
-
D.
Lago Sul
Lago Sul is an affluent residential region of Brasília, Brazil, known for its upscale homes, embassies, and scenic views over Paranoá Lake.
-
E.
Lago Dorado
Lago Dorado is the central lake around which Disney’s Coronado Springs Resort is built, serving as a scenic focal point for the property.
- 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: Lago Cecita Triple: [Sila Mountains, hasLake, Lago Cecita]
Generated description
Lago Cecita is an artificial mountain lake and major reservoir located in the Sila plateau of Calabria, southern Italy.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lago Cecita Target entity description: Lago Cecita is an artificial mountain lake and major reservoir located in the Sila plateau of Calabria, southern Italy.
-
A.
Lago Toro
Lago Toro is a scenic mountain lake located within Chile’s Huerquehue National Park, known for its forested surroundings and hiking access.
-
B.
Gutiérrez Lake
Gutiérrez Lake is a glacial Andean lake in Argentina’s Patagonia region, known for its clear waters, surrounding forests, and outdoor recreation opportunities near Bariloche.
-
C.
Pilarcitos Lake
Pilarcitos Lake is a man-made reservoir on the San Francisco Peninsula that stores water as part of the region’s municipal supply system.
-
D.
Lago Sul
Lago Sul is an affluent residential region of Brasília, Brazil, known for its upscale homes, embassies, and scenic views over Paranoá Lake.
-
E.
Lago Dorado
Lago Dorado is the central lake around which Disney’s Coronado Springs Resort is built, serving as a scenic focal point for the property.
- 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_69d6aa5b0d2881909584b20efc5877f0 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6f860790c81909c2c1d3c489ec5b4 |
completed | April 9, 2026, 12:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d97a9ceea08190944354d127f2c73b |
completed | April 10, 2026, 10:33 p.m. |
| NEDg | Description generation | batch_69d97df755708190bf71d04ead7eaa2c |
completed | April 10, 2026, 10:47 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d97e96d9888190ba693e1df7eb502d |
completed | April 10, 2026, 10:49 p.m. |
Created at: April 8, 2026, 9:08 p.m.