Triple
T19610083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Constanza |
E470707
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Cibao |
—
|
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: Cibao | Statement: [Constanza, region, Cibao]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cibao Context triple: [Constanza, region, Cibao]
-
A.
Cibao
chosen
Cibao is a culturally and economically significant region in the northern Dominican Republic, known for its fertile valleys, agricultural production, and vibrant urban centers like Santiago.
-
B.
Cibatu
Cibatu is a town in West Java, Indonesia, known historically as a railway junction on the route between Bandung and Garut.
-
C.
Liguo
Liguo is a Chinese given name commonly used for males and borne by various individuals across different fields.
-
D.
Cibao Valley
Cibao Valley is a fertile and densely populated agricultural region in the northern Dominican Republic, known as the country’s main breadbasket and economic heartland.
-
E.
Qibao
Qibao is an ancient water town and popular tourist area in Shanghai, known for its historic streets, canals, and traditional architecture.
- 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_69d8e510fa248190b7afb274a1d4cf73 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e640ca57a081909c05000fca52271f |
completed | April 20, 2026, 3:05 p.m. |
Created at: April 10, 2026, 1:43 p.m.