Triple
T12504423
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosario Vera Peñaloza Department |
E298911
|
entity |
| Predicate | hasCapital |
P204
|
FINISHED |
| Object | Chepes |
E298903
|
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: Chepes | Statement: [Rosario Vera Peñaloza Department, hasCapital, Chepes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chepes Context triple: [Rosario Vera Peñaloza Department, hasCapital, Chepes]
-
A.
Chepes
chosen
Chepes is a small city in western Argentina that serves as an important local center within La Rioja Province.
-
B.
Chabreloche
Chabreloche is a small commune in central France’s Puy-de-Dôme department, known for its location in the mountainous Auvergne region.
-
C.
Tiendesitas
Tiendesitas is a popular shopping and lifestyle complex in Pasig, Metro Manila, known for its Filipino-themed architecture, handicrafts, food, and live entertainment.
-
D.
Cresserons
Cresserons is a small commune in the Calvados department of the Normandy region in northwestern France.
-
E.
Barradères
Barradères is a coastal commune and fishing town in Haiti located within the Nippes Department.
- 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_69d6ada4cd388190ae3bbf83ff87057a |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94dfcea188190a929db1aabe1a286 |
completed | April 10, 2026, 7:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f64bb5af708190b3786da334c3bf23 |
completed | May 2, 2026, 7:08 p.m. |
Created at: April 8, 2026, 9:57 p.m.