Triple
T4083667
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oneida Lake |
E87537
|
entity |
| Predicate | fishSpecies |
P7733
|
FINISHED |
| Object | northern pike |
E222688
|
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: northern pike | Statement: [Oneida Lake, fishSpecies, northern pike]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: northern pike Context triple: [Oneida Lake, fishSpecies, northern pike]
-
A.
northern pike
chosen
The northern pike is a large, predatory freshwater fish known for its elongated body, sharp teeth, and ambush-hunting behavior in lakes and rivers across the Northern Hemisphere.
-
B.
Walleye
The walleye is a popular North American freshwater game fish prized by anglers for its excellent taste and challenging sport.
-
C.
Pike
Pike is an English surname of Old English origin, often associated with people who lived near a pointed hill or carried a pike as a weapon.
-
D.
walleye
Walleye is the internal codename used by Google for its Pixel 2 smartphone model.
-
E.
Muskie
Muskie is a surname most notably associated with Edmund Muskie, a prominent American politician and former U.S. Secretary of State.
- 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_69aed9435cf48190ad1da737c962d19d |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefc7933b481909bb3e02c6c04c8ee |
completed | March 9, 2026, 4:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b562c6456081908cca823ebb13936a |
completed | March 14, 2026, 1:29 p.m. |
Created at: March 9, 2026, 3:39 p.m.