Triple
T19501912
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Asunción region |
E487923
|
entity |
| Predicate | hasCoreCity |
P294
|
FINISHED |
| Object | Asunción |
—
|
NE NERFINISHED |
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: Asunción | Statement: [Asunción region, hasCoreCity, Asunción]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Asunción Context triple: [Asunción region, hasCoreCity, Asunción]
-
A.
Asunción
chosen
Asunción is the capital and largest city of Paraguay, located along the Paraguay River and serving as the country’s main political, cultural, and economic center.
-
B.
Asunción
Asunción is the birth name of American lawyer, journalist, and television host Sunny Hostin.
-
C.
Asuncion
Asuncion is a remote volcanic island in the Northern Mariana Islands, known for its steep stratovolcano and relatively undisturbed natural environment.
-
D.
Asuncion
Asuncion is a stage play written by actor and playwright Jesse Eisenberg that explores themes of privilege, prejudice, and cultural misunderstanding.
-
E.
Asuncion
Asuncion is a rural municipality in the province of Davao del Norte on the island of Mindanao in the Philippines.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d9d1c88190b01cd78b8be49384 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6350dbae08190bea7fc3e3eb95c3c |
completed | April 20, 2026, 2:15 p.m. |
Created at: April 10, 2026, 1:40 p.m.