Triple
T9080263
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nottawasaga River |
E217600
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Essa |
E148404
|
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: Essa | Statement: [Nottawasaga River, flowsThrough, Essa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Essa Context triple: [Nottawasaga River, flowsThrough, Essa]
-
A.
Essa
chosen
Essa is a rural township in Simcoe County, Ontario, Canada, known for its agricultural landscape and proximity to the city of Barrie.
-
B.
Nesta
Nesta is the middle name of legendary Jamaican reggae musician and cultural icon Bob Marley.
-
C.
Uma
Uma is an Austronesian language spoken primarily in Central Sulawesi, Indonesia.
-
D.
Uma
Uma is a Bengali film directed by Srijit Mukherji, inspired by a real-life story of a terminally ill girl whose father recreates the Durga Puja festival early so she can experience it.
-
E.
Uma
Uma is a central antagonist in Disney's "Descendants" franchise, known as the ambitious and strong-willed daughter of Ursula who leads a pirate crew on the Isle of the Lost.
- 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_69ca83d7a0388190ba1af89ed7ba36f9 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc9607942c8190a21620892ce3cbe5 |
completed | April 1, 2026, 3:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cffe2103448190bbc09436f19c4f11 |
completed | April 3, 2026, 5:51 p.m. |
Created at: March 30, 2026, 7:13 p.m.