Triple
T12703715
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Albuquerque Basin |
E303525
|
entity |
| Predicate | majorCityInBasin |
P9892
|
FINISHED |
| Object | Belen |
E755437
|
NE FINISHED |
How this triple was built (2 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: Belen | Statement: [Albuquerque Basin, majorCityInBasin, Belen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Belen Context triple: [Albuquerque Basin, majorCityInBasin, Belen]
-
A.
Belen
chosen
Belen is a small city in central New Mexico known as a regional transportation hub and bedroom community for the Albuquerque metropolitan area.
-
B.
Belén
Belén is a town in northwestern Argentina known for its traditional weaving and role as a regional center in Catamarca Province.
-
C.
Gracia
Gracia is a feminine given name, commonly used in Spanish and other Romance languages, that derives from and shares the meaning of the name Grace.
-
D.
Belmonte
Belmonte is a historic town in Portugal known for its medieval castle and strong Jewish heritage, located in the country's Centro Region.
-
E.
Mariental
Mariental is a town in central Namibia that serves as an important agricultural and transport hub in the Hardap Region.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d7bdef90d48190b46b88270e780946 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961f0941081908a879cde0be48667 |
completed | April 10, 2026, 8:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f671b4832c8190abc17f5f33b3552a |
completed | May 2, 2026, 9:50 p.m. |
Created at: April 9, 2026, 5:23 p.m.