Triple
T22866740
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kajaki Dam |
E567075
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Kajaki |
—
|
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: Kajaki | Statement: [Kajaki Dam, locatedIn, Kajaki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kajaki Context triple: [Kajaki Dam, locatedIn, Kajaki]
-
A.
Kajaki
chosen
Kajaki is a town and district in Afghanistan’s Helmand Province, known for its strategic dam and as a focal point of intense military conflict during the Afghan War.
-
B.
Kawki
Kawki is an indigenous Andean language closely related to Aymara and spoken by a small number of people in Peru.
-
C.
Kaiyukan
Kaiyukan is a large, world-renowned public aquarium in Osaka, Japan, famous for its massive central tank and immersive marine life exhibits.
-
D.
Kanajo
Kanajo is a character or entity presented as the counterpart or parallel version of Manajo, typically within the same fictional or conceptual setting.
-
E.
Kagayaki
Kagayaki is the fastest limited-stop train service operating on Japan’s Hokuriku Shinkansen line between Tokyo and the Hokuriku region.
- 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_69e24589083081908d5694c4fdc80086 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17f0210b481908e0e6c4f95ba5a3b |
completed | April 29, 2026, 3:46 a.m. |
Created at: April 17, 2026, 3:38 p.m.