Triple
T17790002
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Huaura Province |
E444131
|
entity |
| Predicate | hasNotableTown |
P14082
|
FINISHED |
| Object |
Végueta
Végueta is a coastal town in Peru’s Huaura Province, known for its fishing activities and nearby archaeological and natural attractions.
|
E1288611
|
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: Végueta | Statement: [Huaura Province, hasNotableTown, Végueta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Végueta Context triple: [Huaura Province, hasNotableTown, Végueta]
-
A.
Zamboanguita
Zamboanguita is a coastal municipality in the Philippine province of Negros Oriental known for its diving spots and proximity to Apo Island.
-
B.
Ouahigouya
Ouahigouya is a major city in northern Burkina Faso known as an important commercial and administrative center of the region.
-
C.
Vegueta
Vegueta is the historic old quarter of Las Palmas de Gran Canaria, known for its colonial architecture, cobbled streets, and cultural landmarks.
-
D.
Guarequena
Guarequena is an alternative name for the Warekena language, an indigenous Arawakan language spoken in parts of Brazil and Venezuela.
-
E.
Marmato
Marmato is a historic Colombian mining town in the Caldas Department, renowned for its centuries-old gold extraction and terraced mountainside setting.
- 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: Végueta Triple: [Huaura Province, hasNotableTown, Végueta]
Generated description
Végueta is a coastal town in Peru’s Huaura Province, known for its fishing activities and nearby archaeological and natural attractions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Végueta Target entity description: Végueta is a coastal town in Peru’s Huaura Province, known for its fishing activities and nearby archaeological and natural attractions.
-
A.
Zamboanguita
Zamboanguita is a coastal municipality in the Philippine province of Negros Oriental known for its diving spots and proximity to Apo Island.
-
B.
Ouahigouya
Ouahigouya is a major city in northern Burkina Faso known as an important commercial and administrative center of the region.
-
C.
Vegueta
Vegueta is the historic old quarter of Las Palmas de Gran Canaria, known for its colonial architecture, cobbled streets, and cultural landmarks.
-
D.
Guarequena
Guarequena is an alternative name for the Warekena language, an indigenous Arawakan language spoken in parts of Brazil and Venezuela.
-
E.
Marmato
Marmato is a historic Colombian mining town in the Caldas Department, renowned for its centuries-old gold extraction and terraced mountainside setting.
- 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_69e4879688908190a5428b1fa7525f62 |
completed | April 19, 2026, 7:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a02f83854388190bfa27b6adbf2b364 |
completed | May 12, 2026, 9:51 a.m. |
| NEDg | Description generation | batch_6a02f9e9f38c81908de58994c086b52f |
completed | May 12, 2026, 9:59 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a02fadc5d4481908f7e7139a83c02bc |
completed | May 12, 2026, 10:03 a.m. |
Created at: April 10, 2026, 10:13 a.m.