Triple
T11403291
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Azuay Province |
E270169
|
entity |
| Predicate | hasImportantTown |
P14082
|
FINISHED |
| Object |
Santa Isabel
Santa Isabel is a town in southern Ecuador’s Azuay Province, known as a local commercial and agricultural center in the region.
|
E923843
|
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: Santa Isabel | Statement: [Azuay Province, hasImportantTown, Santa Isabel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Santa Isabel Context triple: [Azuay Province, hasImportantTown, Santa Isabel]
-
A.
Santa Isabel
Santa Isabel is a supermarket chain in Latin America operated under the retail group Cencosud.
-
B.
Santa Isabel
Santa Isabel was the colonial capital city of Spanish Equatorial Guinea, serving as the administrative and political center during Spanish rule.
-
C.
Santa Isabel
Santa Isabel was a Spanish expedition ship associated with the Santa Cruz colony during the era of New World exploration.
-
D.
Santa Isabel
Santa Isabel is a coastal municipality in southern Puerto Rico known for its agricultural production, particularly sugarcane and plantains.
-
E.
Santa Elena
Santa Elena was a 16th-century Spanish colonial settlement on present-day Parris Island, South Carolina, that served as the capital of Spanish Florida for a time.
- 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: Santa Isabel Triple: [Azuay Province, hasImportantTown, Santa Isabel]
Generated description
Santa Isabel is a town in southern Ecuador’s Azuay Province, known as a local commercial and agricultural center in the region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Santa Isabel Target entity description: Santa Isabel is a town in southern Ecuador’s Azuay Province, known as a local commercial and agricultural center in the region.
-
A.
Santa Isabel
Santa Isabel was the colonial capital city of Spanish Equatorial Guinea, serving as the administrative and political center during Spanish rule.
-
B.
Santa Isabel
Santa Isabel was a Spanish expedition ship associated with the Santa Cruz colony during the era of New World exploration.
-
C.
Santa Isabel
Santa Isabel is a supermarket chain in Latin America operated under the retail group Cencosud.
-
D.
Santa Isabel
Santa Isabel is a coastal municipality in southern Puerto Rico known for its agricultural production, particularly sugarcane and plantains.
-
E.
Santa Elena
Santa Elena was a 16th-century Spanish colonial settlement on present-day Parris Island, South Carolina, that served as the capital of Spanish Florida for a time.
- 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_69d6aaddeaa8819088b30ef7b50598c9 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8014ab46881909fa1d425926c617b |
completed | April 9, 2026, 7:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e58d244870819091e8331eb3bd792d |
completed | April 20, 2026, 2:19 a.m. |
| NEDg | Description generation | batch_69e59777b1208190a33a50da286535ee |
completed | April 20, 2026, 3:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e5a3cf9d388190944340af484b3a54 |
completed | April 20, 2026, 3:55 a.m. |
Created at: April 8, 2026, 9:34 p.m.