Triple
T17777157
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diguillín Province |
E443800
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object |
El Carmen
El Carmen is a small town in Chile’s Ñuble Region, situated in the rural interior of Diguillín Province.
|
E1287890
|
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: El Carmen | Statement: [Diguillín Province, hasTown, El Carmen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: El Carmen Context triple: [Diguillín Province, hasTown, El Carmen]
-
A.
El Carmen
El Carmen is a town in coastal Ecuador known as an agricultural and commercial center within Manabí Province.
-
B.
San Pedrito
San Pedrito is a subway station in Buenos Aires that serves as the western terminus of the city's historic Line A.
-
C.
Barranquitas
Barranquitas is a mountainous inland municipality of Puerto Rico known for its cool climate, scenic views, and traditional cultural festivals.
-
D.
San Cristóbal de la Barranca
San Cristóbal de la Barranca is a small municipality in the state of Jalisco, Mexico, known for its deep canyon landscapes and hot springs along the Santiago River.
-
E.
Candelaria
Candelaria is a coastal town and important pilgrimage center on the island of Tenerife in Spain’s Canary Islands, known for the Basilica of Our Lady of Candelaria.
- 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: El Carmen Triple: [Diguillín Province, hasTown, El Carmen]
Generated description
El Carmen is a small town in Chile’s Ñuble Region, situated in the rural interior of Diguillín Province.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: El Carmen Target entity description: El Carmen is a small town in Chile’s Ñuble Region, situated in the rural interior of Diguillín Province.
-
A.
El Carmen
El Carmen is a town in coastal Ecuador known as an agricultural and commercial center within Manabí Province.
-
B.
San Pedrito
San Pedrito is a subway station in Buenos Aires that serves as the western terminus of the city's historic Line A.
-
C.
Barranquitas
Barranquitas is a mountainous inland municipality of Puerto Rico known for its cool climate, scenic views, and traditional cultural festivals.
-
D.
San Cristóbal de la Barranca
San Cristóbal de la Barranca is a small municipality in the state of Jalisco, Mexico, known for its deep canyon landscapes and hot springs along the Santiago River.
-
E.
Candelaria
Candelaria is a coastal town and important pilgrimage center on the island of Tenerife in Spain’s Canary Islands, known for the Basilica of Our Lady of Candelaria.
- 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_69d8b9ef17708190bdf7e2adbf14ddc2 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4871e06a481909cf6d59e49dc21c5 |
completed | April 19, 2026, 7:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a02f8321e288190be4228b6975f372d |
completed | May 12, 2026, 9:51 a.m. |
| NEDg | Description generation | batch_6a02f94b4748819096a529f04525cf03 |
completed | May 12, 2026, 9:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a02fa28ac0881909d9d4273ea576273 |
completed | May 12, 2026, 10 a.m. |
Created at: April 10, 2026, 10:12 a.m.