Triple
T23089956
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Russian Wilderness |
E575716
|
entity |
| Predicate | hasFeature |
P182
|
FINISHED |
| Object |
Sugar Lake
Sugar Lake is a scenic alpine lake located within the remote, rugged Russian Wilderness of northern California.
|
E1570196
|
NE FINISHED |
How this triple was built (4 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: Sugar Lake | Statement: [Russian Wilderness, hasFeature, Sugar Lake]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sugar Lake Context triple: [Russian Wilderness, hasFeature, Sugar Lake]
-
A.
Odiongan
Odiongan is a coastal municipality on Tablas Island in Romblon province, Philippines, serving as a local commercial and transportation hub in the region.
-
B.
City of Lakes
City of Lakes is a popular nickname for Thane, a city in Maharashtra, India, known for its numerous lakes and scenic waterfronts.
-
C.
City of Lakes
City of Lakes is the nickname for Dartmouth, Nova Scotia, highlighting its numerous surrounding lakes and waterfronts.
-
D.
City of Lakes
City of Lakes is a popular nickname for Minneapolis, highlighting its many urban lakes and waterfronts.
-
E.
City of Lakes
City of Lakes is a popular nickname for Udaipur, a picturesque city in Rajasthan, India, renowned for its numerous interconnected lakes and romantic waterfront scenery.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Sugar Lake Triple: [Russian Wilderness, hasFeature, Sugar Lake]
Generated description
Sugar Lake is a scenic alpine lake located within the remote, rugged Russian Wilderness of northern California.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sugar Lake Target entity description: Sugar Lake is a scenic alpine lake located within the remote, rugged Russian Wilderness of northern California.
-
A.
Odiongan
Odiongan is a coastal municipality on Tablas Island in Romblon province, Philippines, serving as a local commercial and transportation hub in the region.
-
B.
City of Lakes
City of Lakes is a popular nickname for Thane, a city in Maharashtra, India, known for its numerous lakes and scenic waterfronts.
-
C.
City of Lakes
City of Lakes is the nickname for Dartmouth, Nova Scotia, highlighting its numerous surrounding lakes and waterfronts.
-
D.
City of Lakes
City of Lakes is a popular nickname for Minneapolis, highlighting its many urban lakes and waterfronts.
-
E.
City of Lakes
City of Lakes is a popular nickname for Udaipur, a picturesque city in Rajasthan, India, renowned for its numerous interconnected lakes and romantic waterfront scenery.
- F. None of above. chosen
Provenance (5 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_69e245bf3e3c819086d3448720efc01b |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18da99bec81908ecadf8dab10d1b7 |
completed | April 29, 2026, 4:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c15b77f7c8190b374a581b776d155 |
completed | May 19, 2026, 7:48 a.m. |
| NEDg | Description generation | batch_6a0c17791f8481909f0f42f9122e0c3a |
completed | May 19, 2026, 7:55 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c1863ea2c81909345cb5701fcd8ed |
completed | May 19, 2026, 7:59 a.m. |
Created at: April 17, 2026, 3:57 p.m.