Triple
T21174156
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Corrientes Province |
E521763
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object |
Goya
Goya is a city in northeastern Argentina known for its agricultural production, especially tobacco, and its annual National Surubí Fishing Festival.
|
E1470165
|
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: Goya | Statement: [Corrientes Province, hasMajorCity, Goya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Goya Context triple: [Corrientes Province, hasMajorCity, Goya]
-
A.
Goya
Goya is Habana Labs’ AI inference processor designed to accelerate deep learning workloads with high efficiency and scalability.
-
B.
Goya
Goya is a central, upscale neighborhood in Madrid, Spain, known for its shopping streets, cultural venues, and sports arenas.
-
C.
Goya Toledo
Goya Toledo is a Spanish actress and former model best known internationally for her role in the acclaimed film "Amores perros."
-
D.
Francisco Goya
Francisco Goya was a pioneering Spanish Romantic painter and printmaker renowned for his powerful portraits, dark and haunting imagery, and critical depictions of war and society.
-
E.
Velásquez
Velásquez is a Spanish-language surname common in Hispanic countries and among people of Spanish or Latin American heritage.
- 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: Goya Triple: [Corrientes Province, hasMajorCity, Goya]
Generated description
Goya is a city in northeastern Argentina known for its agricultural production, especially tobacco, and its annual National Surubí Fishing Festival.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Goya Target entity description: Goya is a city in northeastern Argentina known for its agricultural production, especially tobacco, and its annual National Surubí Fishing Festival.
-
A.
Goya
Goya is Habana Labs’ AI inference processor designed to accelerate deep learning workloads with high efficiency and scalability.
-
B.
Goya
Goya is a central, upscale neighborhood in Madrid, Spain, known for its shopping streets, cultural venues, and sports arenas.
-
C.
Goya Toledo
Goya Toledo is a Spanish actress and former model best known internationally for her role in the acclaimed film "Amores perros."
-
D.
Francisco Goya
Francisco Goya was a pioneering Spanish Romantic painter and printmaker renowned for his powerful portraits, dark and haunting imagery, and critical depictions of war and society.
-
E.
Velásquez
Velásquez is a Spanish-language surname common in Hispanic countries and among people of Spanish or Latin American heritage.
- 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_69e0b50e30748190b186824a206d39b9 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e72714d3f48190871c5e35c3887d7f |
completed | April 21, 2026, 7:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09757599708190b03ebb61a54b0c2a |
completed | May 17, 2026, 7:59 a.m. |
| NEDg | Description generation | batch_6a097664811c81908e9bb1dfcf88b042 |
completed | May 17, 2026, 8:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0978130b1481909e7a7c7a43f82c15 |
completed | May 17, 2026, 8:10 a.m. |
Created at: April 16, 2026, 3 p.m.