Triple
T6012747
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cachapoal Province |
E133873
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object | Rengo |
E614791
|
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: Rengo | Statement: [Cachapoal Province, hasMunicipality, Rengo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rengo Context triple: [Cachapoal Province, hasMunicipality, Rengo]
-
A.
Rengo
chosen
Rengo is a Chilean city in the O'Higgins Region known for its agricultural activity and role as a local commercial center.
-
B.
Towa
Towa is a Native American Tanoan language spoken primarily by the Jemez Pueblo people of northern New Mexico.
-
C.
Koromo
Koromo was the former name of what is now Toyota City in Aichi Prefecture, Japan, historically known as a regional center before becoming synonymous with the Toyota automobile company.
-
D.
Wakatsuki
Wakatsuki was a Japanese destroyer of the Imperial Japanese Navy that served in World War II before being sunk during late-war Pacific naval operations.
-
E.
Mitaka
Mitaka is a city in western Tokyo, Japan, known for its residential neighborhoods, parks, and the Ghibli Museum.
- 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_69c0087361a48190905c6b55969852b8 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04f528acc8190bc6943d812460b57 |
completed | March 22, 2026, 8:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c74872582c81908c64a9bf925f67c6 |
completed | March 28, 2026, 3:18 a.m. |
Created at: March 22, 2026, 4:06 p.m.