Triple
T15063970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Babysitter |
E379707
|
entity |
| Predicate | cinematography |
P1953
|
FINISHED |
| Object | Shane Hurlbut |
E138142
|
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: Shane Hurlbut | Statement: [The Babysitter, cinematography, Shane Hurlbut]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shane Hurlbut Context triple: [The Babysitter, cinematography, Shane Hurlbut]
-
A.
Shane Hurlbut
chosen
Shane Hurlbut is an American cinematographer known for his work on major Hollywood films and his influential role in advancing digital cinematography techniques.
-
B.
Shane Boris
Shane Boris is an Academy Award–winning American film producer known for his work on acclaimed documentaries such as "Navalny."
-
C.
Michael Pitts
Michael Pitts is a relatively common personal name shared by multiple individuals, including figures in fields such as politics, religion, and entertainment.
-
D.
Shane Vendrell
Shane Vendrell is a volatile and morally compromised detective on the TV series "The Shield," known for his loyalty to Vic Mackey and his descent into increasingly tragic and violent choices.
-
E.
Scott Shane
Scott Shane is an American academic and author renowned for his influential research in entrepreneurship and small business management.
- 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_69d85cd7683881908d405c1b5d7b4f7f |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69dedee803ac81908bb7d66e49c2eb72 |
completed | April 15, 2026, 12:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69febfdc8f64819083c7e3510e671b9a |
completed | May 9, 2026, 5:02 a.m. |
Created at: April 10, 2026, 3:02 a.m.