Triple
T4049663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Punta del Este |
E84152
|
entity |
| Predicate | hasNearbyLocality |
P3883
|
FINISHED |
| Object |
Maldonado
Maldonado is a coastal city in southeastern Uruguay that serves as the capital of Maldonado Department and a key urban center near the resort town of Punta del Este.
|
E409138
|
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: Maldonado | Statement: [Punta del Este, hasNearbyLocality, Maldonado]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maldonado Context triple: [Punta del Este, hasNearbyLocality, Maldonado]
-
A.
Rojas
Rojas is a Spanish surname historically associated with prominent noble families and political figures in Spain.
-
B.
Garzón
Garzón is a municipality and town in south-central Colombia known as an agricultural center within the Huila Department.
-
C.
Herrera
Herrera is a common Spanish surname borne by numerous notable figures across sports, politics, arts, and other fields in the Spanish-speaking world.
-
D.
Magaña
Magaña is a Spanish-language surname of Hispanic origin borne by various notable individuals in Mexico and other Spanish-speaking countries.
-
E.
Balderas
Balderas is a major Mexico City Metro station known for its central location and high passenger traffic.
- 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: Maldonado Triple: [Punta del Este, hasNearbyLocality, Maldonado]
Generated description
Maldonado is a coastal city in southeastern Uruguay that serves as the capital of Maldonado Department and a key urban center near the resort town of Punta del Este.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Maldonado Target entity description: Maldonado is a coastal city in southeastern Uruguay that serves as the capital of Maldonado Department and a key urban center near the resort town of Punta del Este.
-
A.
Rojas
Rojas is a Spanish surname historically associated with prominent noble families and political figures in Spain.
-
B.
Garzón
Garzón is a municipality and town in south-central Colombia known as an agricultural center within the Huila Department.
-
C.
Herrera
Herrera is a common Spanish surname borne by numerous notable figures across sports, politics, arts, and other fields in the Spanish-speaking world.
-
D.
Magaña
Magaña is a Spanish-language surname of Hispanic origin borne by various notable individuals in Mexico and other Spanish-speaking countries.
-
E.
Balderas
Balderas is a major Mexico City Metro station known for its central location and high passenger traffic.
- 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_69aed930bd5c819083e7dcc14fc44f69 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefb82d1a08190aa8c5c48d368b58b |
completed | March 9, 2026, 4:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b55656ecbc819093f23636a7f72f36 |
completed | March 14, 2026, 12:36 p.m. |
| NEDg | Description generation | batch_69b55718acb88190a491e9654c1f1b7f |
completed | March 14, 2026, 12:39 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b55785a6b4819083737f26eb5db217 |
completed | March 14, 2026, 12:41 p.m. |
Created at: March 9, 2026, 3:37 p.m.