Triple
T468130
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catamarca Province |
E8493
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Belén
Belén is a town in northwestern Argentina known for its traditional weaving and role as a regional center in Catamarca Province.
|
E60654
|
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: Belén | Statement: [Catamarca Province, contains, Belén]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Belén Context triple: [Catamarca Province, contains, Belén]
-
A.
San Borja
San Borja is a primarily residential and commercial district in Lima, Peru, known for its middle- to upper-class neighborhoods, green areas, and cultural institutions.
-
B.
León
León is a historic city and former kingdom in northwestern Spain, renowned for its medieval architecture and significant role in the formation of the Spanish state.
-
C.
Ubaté
Ubaté is a town and municipality in central Colombia known for its dairy production and colonial-era architecture.
-
D.
San Isidro
San Isidro is an upscale, modern district of Lima, Peru, known for its financial center, embassies, parks, and high-end residential areas.
-
E.
Córdoba, Argentina
Córdoba, Argentina is a major city in central Argentina known for its colonial architecture, historic universities, and role as an important cultural and economic center.
- 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: Belén Triple: [Catamarca Province, contains, Belén]
Generated description
Belén is a town in northwestern Argentina known for its traditional weaving and role as a regional center in Catamarca Province.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Belén Target entity description: Belén is a town in northwestern Argentina known for its traditional weaving and role as a regional center in Catamarca Province.
-
A.
San Borja
San Borja is a primarily residential and commercial district in Lima, Peru, known for its middle- to upper-class neighborhoods, green areas, and cultural institutions.
-
B.
León
León is a historic city and former kingdom in northwestern Spain, renowned for its medieval architecture and significant role in the formation of the Spanish state.
-
C.
Ubaté
Ubaté is a town and municipality in central Colombia known for its dairy production and colonial-era architecture.
-
D.
San Isidro
San Isidro is an upscale, modern district of Lima, Peru, known for its financial center, embassies, parks, and high-end residential areas.
-
E.
Córdoba, Argentina
Córdoba, Argentina is a major city in central Argentina known for its colonial architecture, historic universities, and role as an important cultural and economic center.
- 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_69a2e7f3aeb48190a19453e3a043f486 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2efd9bea081909ee782840f3da12b |
completed | Feb. 28, 2026, 1:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4711aeae88190924a592e4979a356 |
completed | March 1, 2026, 5:02 p.m. |
| NEDg | Description generation | batch_69a472f1b4988190aeb0102e11c69ac6 |
completed | March 1, 2026, 5:10 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a473742eac8190a48052bb14cfb7e5 |
completed | March 1, 2026, 5:12 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.