Triple
T3317056
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hai River |
E69706
|
entity |
| Predicate | hasTributary |
P415
|
FINISHED |
| Object | Ziya River |
E420752
|
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: Ziya River | Statement: [Hai River, hasTributary, Ziya River]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ziya River Context triple: [Hai River, hasTributary, Ziya River]
-
A.
Ziya River
chosen
The Ziya River is a major river in northern China that serves as one of the principal tributaries feeding into the Hai River system.
-
B.
Kilmez River
The Kilmez River is a waterway in Russia that serves as a tributary within the Kama River basin.
-
C.
Keles River
The Keles River is a Central Asian watercourse that flows through Kazakhstan and Uzbekistan before joining the Syr Darya.
-
D.
Leysse River
The Leysse River is a watercourse in the Savoie region of southeastern France that flows through Chambéry before emptying into Lake Bourget.
-
E.
Turka River
The Turka River is a Siberian river that flows into Lake Baikal, contributing to the lake’s freshwater inflow system.
- 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_69ad85a0bb048190a5458d2738012d61 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb11230b881908f5b554323729cc5 |
completed | March 8, 2026, 5:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5a81fdf888190a4d9f75471b9ec39 |
completed | March 14, 2026, 6:25 p.m. |
Created at: March 8, 2026, 3:11 p.m.