Triple
T5159036
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elysian Park |
E116386
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Silver Lake |
E45555
|
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: Silver Lake | Statement: [Elysian Park, near, Silver Lake]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Silver Lake Context triple: [Elysian Park, near, Silver Lake]
-
A.
Silver Lake
Silver Lake is a scenic recreational lake located within Blackwell Forest Preserve in DuPage County, Illinois.
-
B.
Silver Lake
Silver Lake is a residential neighborhood on Staten Island in New York City, known for its namesake park and reservoir.
-
C.
Silver Lake
Silver Lake is a leading global private equity firm specializing in technology and technology-enabled investments.
-
D.
Silver Lake
Silver Lake is a small freshwater lake located in Wilmington, Massachusetts, known locally for recreation and scenic views.
-
E.
Silver Lake
chosen
Silver Lake is a scenic body of water within Lynn Woods Reservation in Lynn, Massachusetts, popular for its natural beauty and outdoor recreation.
- 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_69bd445edb3881909b93b34d260717fc |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7904457c819090f382029ebb2b43 |
completed | March 20, 2026, 4:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69dbb6bb34548190aac9d2af05477750 |
completed | April 12, 2026, 3:14 p.m. |
Created at: March 20, 2026, 1:44 p.m.