Triple
T23449440
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Larisa Halpert |
E567733
|
entity |
| Predicate | appearsInEpisode |
P795
|
FINISHED |
| Object | Niagara |
—
|
NE NERFINISHED |
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 | Statement: [Larisa Halpert, appearsInEpisode, Niagara]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Niagara Context triple: [Larisa Halpert, appearsInEpisode, Niagara]
-
A.
Niagara
Niagara is a small rural city located in Grand Forks County in the U.S. state of North Dakota.
-
B.
Niagara
Niagara is a cold-hardy, labrusca-based white grape variety widely grown in the eastern United States, known for its distinctive “foxy” aroma and use in sweet and table wines.
-
C.
Niagara
Niagara is the codename for Sun Microsystems' UltraSPARC T1 multicore, multithreaded server processor designed for high-throughput, low-power computing.
-
D.
Niagara
"Niagara" is a track by Barbra Streisand featured on her 1979 disco-influenced album "Wet."
-
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. chosen
Provenance (2 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_69e2458b4c888190b1d7998f9862a558 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1a64c1ee081908ba3adf5ce80d6a5 |
completed | April 29, 2026, 6:33 a.m. |
Created at: April 17, 2026, 5:52 p.m.