Triple
T13541286
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sarah Sanderson |
E323390
|
entity |
| Predicate | targetOf |
P860
|
FINISHED |
| Object | Dani Dennison |
E323392
|
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: Dani Dennison | Statement: [Sarah Sanderson, targetOf, Dani Dennison]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dani Dennison Context triple: [Sarah Sanderson, targetOf, Dani Dennison]
-
A.
Dani Dennison
chosen
Dani Dennison is a brave and spirited young girl in the film "Hocus Pocus" who helps her brother battle resurrected witches on Halloween night.
-
B.
Harmony Parker
Harmony Parker is the imaginative young girl who discovers a magical 50p coin that grants wishes in the children's book and TV series "The Queen's Nose."
-
C.
Samara Morgan
Samara Morgan is the vengeful ghostly girl from the horror film "The Ring," known for her cursed videotape and terrifying emergence from television screens.
-
D.
Emma Eames
Emma Eames was a celebrated American soprano of the late 19th and early 20th centuries, renowned for her performances at the Metropolitan Opera and major European opera houses.
-
E.
Hollis Mulwray
Hollis Mulwray is a key character in the film "Chinatown," serving as Los Angeles's chief water engineer whose mysterious death drives the movie's central investigation.
- 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_69d8076776248190bdf0d4fa1f85a5fc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbafd8ba10819098faadcc6adf251e |
completed | April 12, 2026, 2:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f77f84935c8190b9e41f44140066e5 |
completed | May 3, 2026, 5:01 p.m. |
Created at: April 9, 2026, 9:45 p.m.