Triple
T21417495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Buin Zoo |
E528342
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Buin |
—
|
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: Buin | Statement: [Buin Zoo, locatedIn, Buin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Buin Context triple: [Buin Zoo, locatedIn, Buin]
-
A.
Buin
chosen
Buin is a Chilean town and commune located south of Santiago, known for its agricultural activity and the Buin Zoo.
-
B.
Buin
Buin are an indigenous ethnic group from the southern part of Bougainville Island in Papua New Guinea, known for their distinct language and cultural traditions.
-
C.
Bilma
Bilma is an oasis town in northeastern Niger, historically significant as a key stop on trans-Saharan trade routes and known for its traditional salt production.
-
D.
Bakhdida
Bakhdida is a historically Assyrian Christian town in northern Iraq, known for its ancient churches and location in the Nineveh Plains near Mosul.
-
E.
Buharkent
Buharkent is a small district and town in western Turkey known for its geothermal resources and agricultural production within Aydın Province.
- 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_69e0c454c248819093425d1099101c09 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e8b2073a7881909adda8ed70a2cecd |
completed | April 22, 2026, 11:33 a.m. |
Created at: April 16, 2026, 5:46 p.m.