Triple
T14610468
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taylor Kitsch |
E342944
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Battleship (film) |
E85477
|
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: Battleship (film) | Statement: [Taylor Kitsch, notableWork, Battleship (film)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Battleship (film) Context triple: [Taylor Kitsch, notableWork, Battleship (film)]
-
A.
Battleship
chosen
Battleship is a 2012 science fiction naval war film loosely inspired by the classic board game, featuring an alien invasion and large-scale maritime combat.
-
B.
film "Battleship"
The film "Battleship" is a 2012 science fiction naval war movie loosely based on the classic board game, featuring a U.S. fleet battling an alien invasion.
-
C.
Battleship No. 1
Battleship No. 1 was the original designation for the Imperial Japanese Navy’s Nagato, a pioneering fast battleship that served as a flagship and symbol of Japan’s naval power in the early 20th century.
-
D.
The Marine
The Marine is a 2006 action film starring professional wrestler and actor John Cena as a former Marine on a mission to rescue his kidnapped wife.
-
E.
Convoy
Convoy is a technology-driven freight and logistics startup that built a digital marketplace to connect shippers with trucking carriers.
- 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_69d822dec68081908c2553145c4051dc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb44f0dd48190a78662b5998a6722 |
completed | April 14, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fda91f437c8190ada4d1c3708faedd |
completed | May 8, 2026, 9:13 a.m. |
Created at: April 10, 2026, 1:25 a.m.