Triple
T21316623
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kabul Province |
E525488
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Surobi |
—
|
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: Surobi | Statement: [Kabul Province, hasMajorCity, Surobi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Surobi Context triple: [Kabul Province, hasMajorCity, Surobi]
-
A.
Surobi
chosen
Surobi is a town and district in eastern Afghanistan, strategically located along the Kabul River and a key site for nearby hydroelectric infrastructure.
-
B.
Sorabi
Sorabi is a traditional Indonesian pancake made from rice flour and coconut milk, commonly enjoyed as a snack or street food.
-
C.
Shobijin
Shobijin are the tiny twin priestesses who serve as Mothra’s mystical spokespeople and guardians in the Godzilla and broader kaiju film universe.
-
D.
Sawa
Sawa is a Japanese surname most prominently associated with Homare Sawa, a legendary Japanese women’s footballer and World Cup winner.
-
E.
Saiun
Saiun is the Allied reporting name for the Nakajima C6N, a fast and long-range Japanese carrier-based reconnaissance aircraft used during World War II.
- 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_69e0b51ad810819098c12392c8e55f6c |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e75dd0b34481909dd6d37cf8e42144 |
completed | April 21, 2026, 11:21 a.m. |
Created at: April 16, 2026, 4:30 p.m.