Triple
T16445009
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Overboard |
E399401
|
entity |
| Predicate | title |
P38
|
FINISHED |
| Object | Overboard |
E399401
|
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: Overboard | Statement: [Overboard, title, Overboard]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Overboard Context triple: [Overboard, title, Overboard]
-
A.
Overboard
chosen
Overboard is a 1987 romantic comedy film starring Goldie Hawn and Kurt Russell, known for its amnesia-based plot and lighthearted take on class differences and family.
-
B.
The Wreck
The Wreck is a popular surf break off Byron Bay, Australia, known for the shipwreck that shapes its waves and distinctive lineup.
-
C.
Lost at Sea
Lost at Sea is a collaborative R&B mixtape by singer Jacquees and producer Birdman, blending smooth vocals with melodic trap-influenced production.
-
D.
Wrecked
"Wrecked" is a mystery novel by American author Carol Higgins Clark, featuring her recurring sleuth Regan Reilly in a lighthearted, suspenseful whodunit.
-
E.
Wrecked
Wrecked is a comedic short film directed by Eric Kissack, known for its dark humor and twist-driven narrative.
- 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_69d87f2c6778819080fcfae53be8f12a |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32cdb5d908190bb6c5cb3c794cf4b |
completed | April 18, 2026, 7:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a004f4b738881908f8a205466397f33 |
completed | May 10, 2026, 9:26 a.m. |
Created at: April 10, 2026, 5:10 a.m.