Triple
T3555710
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United Provinces of the Río de la Plata |
E75213
|
entity |
| Predicate | includedTerritory |
P285
|
FINISHED |
| Object |
San Luis
San Luis is a province in central Argentina known for its mountainous landscapes, arid climate, and role in the country’s early independence era.
|
E366492
|
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: San Luis | Statement: [United Provinces of the Río de la Plata, includedTerritory, San Luis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: San Luis Context triple: [United Provinces of the Río de la Plata, includedTerritory, San Luis]
-
A.
San Luis
San Luis is a residential and commercial district located in the eastern part of Lima, Peru.
-
B.
San Luis
San Luis is a municipality and town in western Cuba known for its agricultural activities within Pinar del Río Province.
-
C.
Santa Fe
Santa Fe is a major modern business and financial district in western Mexico City known for its corporate offices, upscale shopping centers, and contemporary high-rise architecture.
-
D.
Santa Fe
Santa Fe is a town on Cuba’s Isla de la Juventud, known as one of the island’s principal local settlements.
-
E.
Santa Fe
Santa Fe is a coastal municipality on Bantayan Island in Cebu, Philippines, known for its white-sand beaches and laid-back island atmosphere.
- 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: San Luis Triple: [United Provinces of the Río de la Plata, includedTerritory, San Luis]
Generated description
San Luis is a province in central Argentina known for its mountainous landscapes, arid climate, and role in the country’s early independence era.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: San Luis Target entity description: San Luis is a province in central Argentina known for its mountainous landscapes, arid climate, and role in the country’s early independence era.
-
A.
San Luis
San Luis is a residential and commercial district located in the eastern part of Lima, Peru.
-
B.
San Luis
San Luis is a municipality and town in western Cuba known for its agricultural activities within Pinar del Río Province.
-
C.
Santa Fe
Santa Fe is a major modern business and financial district in western Mexico City known for its corporate offices, upscale shopping centers, and contemporary high-rise architecture.
-
D.
Santa Fe
Santa Fe is a town on Cuba’s Isla de la Juventud, known as one of the island’s principal local settlements.
-
E.
Santa Fe
Santa Fe is a coastal municipality on Bantayan Island in Cebu, Philippines, known for its white-sand beaches and laid-back island atmosphere.
- 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_69ad85d45090819086f34fb85d850a1e |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc0569fbc81909b855b6990c1415b |
completed | March 8, 2026, 6:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b38bf40dac8190837053dd315303af |
completed | March 13, 2026, 4 a.m. |
| NEDg | Description generation | batch_69b38c6e70a88190805de417a8740d64 |
completed | March 13, 2026, 4:02 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b38cd2c540819083d3188c2dea7283 |
completed | March 13, 2026, 4:04 a.m. |
Created at: March 8, 2026, 3:20 p.m.