Triple
T4446754
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Niagara |
E96306
|
entity |
| Predicate | setting |
P1957
|
FINISHED |
| Object | Niagara Falls |
E10589
|
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: Niagara Falls | Statement: [Niagara, setting, Niagara Falls]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Niagara Falls Context triple: [Niagara, setting, Niagara Falls]
-
A.
Niagara Falls
chosen
Niagara Falls is a famous group of massive waterfalls on the border between the United States and Canada, renowned for their impressive volume and natural beauty.
-
B.
Horseshoe Falls
Horseshoe Falls is a distinct curved segment of Victoria Falls known for its dramatic, horseshoe-shaped curtain of water.
-
C.
Horseshoe Falls
Horseshoe Falls is the largest and most famous of the three waterfalls that collectively form Niagara Falls, straddling the border between Canada and the United States.
-
D.
The Falls
The Falls is a novel by Joyce Carol Oates that explores the aftermath of a tragic event at Niagara Falls and its impact on a family over several decades.
-
E.
Niagara
Niagara is a 1953 film noir thriller starring Marilyn Monroe, noted for its dramatic use of the Niagara Falls setting and Monroe’s breakout femme fatale performance.
- 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_69b345415ba481908df738e7174448ba |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b355d31e10819086590b9f828d50b0 |
completed | March 13, 2026, 12:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b66b31c9488190bc24f802fff24073 |
completed | March 15, 2026, 8:17 a.m. |
Created at: March 12, 2026, 11:32 p.m.