Triple
T13510448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Limmat Valley |
E321125
|
entity |
| Predicate | hydrologicalFeature |
P1778
|
FINISHED |
| Object | Limmat |
E70156
|
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: Limmat | Statement: [Limmat Valley, hydrologicalFeature, Limmat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Limmat Context triple: [Limmat Valley, hydrologicalFeature, Limmat]
-
A.
Limmat
chosen
The Limmat is a Swiss river that flows out of Lake Zurich through the city of Zurich and continues northward until it joins the Aare.
-
B.
Laimosemion
Laimosemion is a genus of small, brightly colored Neotropical killifishes commonly found in freshwater habitats of northern South America.
-
C.
Luntai
Luntai is a small oasis town and county in China’s Xinjiang region, situated along the northern edge of the Taklamakan Desert on the historic Silk Road.
-
D.
Lupaus
Lupaus is a Finnish film for which composer Tuomas Kantelinen created the musical score.
-
E.
Maluma
Maluma is a Colombian reggaeton and Latin pop singer-songwriter known for his chart-topping hits and major influence in contemporary Latin music.
- 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_69d807629d6c8190998f1b9bb12d2ed0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaf86a6208190be8c18f7a0158f23 |
completed | April 12, 2026, 2:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f75d91e35881909a7184be0ad70c14 |
completed | May 3, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:43 p.m.