Triple
T11377411
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vihar Lake |
E269505
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Tulsi Lake |
E268101
|
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: Tulsi Lake | Statement: [Vihar Lake, locatedNear, Tulsi Lake]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tulsi Lake Context triple: [Vihar Lake, locatedNear, Tulsi Lake]
-
A.
Tulsi Lake
chosen
Tulsi Lake is a freshwater reservoir on Salsette Island in Mumbai, India, that serves as one of the city's important sources of drinking water.
-
B.
Lake Buchanan
Lake Buchanan is a large Highland Lakes reservoir in Central Texas known for its recreational fishing, boating, and scenic Hill Country surroundings.
-
C.
Tamina
The Tamina is a river in eastern Switzerland known for flowing through the deep Tamina Gorge before joining the Alpine Rhine.
-
D.
Marla
Marla is a feminine given name most notably borne by American actress and television personality Marla Maples.
-
E.
Alice Wyth Lake
Alice Wyth Lake is a recreational lake in Iowa known for activities like fishing, boating, and wildlife viewing within George Wyth State Park.
- 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_69d6aacca1048190b39dbbc2174616fa |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7ea8e6d44819095f949581421e98e |
completed | April 9, 2026, 6:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e55697b2388190929d7e0b15d809ba |
completed | April 19, 2026, 10:26 p.m. |
Created at: April 8, 2026, 9:33 p.m.