Triple
T16850299
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Lego Ninjago Movie |
E409656
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Bob Logan |
E367213
|
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: Bob Logan | Statement: [The Lego Ninjago Movie, screenwriter, Bob Logan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bob Logan Context triple: [The Lego Ninjago Movie, screenwriter, Bob Logan]
-
A.
Bob Logan
chosen
Bob Logan is a film director and screenwriter best known for co-directing the animated feature "The Lego Ninjago Movie."
-
B.
Kenneth Logan
Kenneth Logan is a fictional character portrayed by British actor Steve Toussaint, likely appearing in film or television drama.
-
C.
Dennis Morgan
Dennis Morgan was an American film actor and singer best known for his leading-man roles in Hollywood musicals and dramas of the 1940s.
-
D.
Steve Logan
Steve Logan is the central protagonist of Ken Follett’s thriller novel "The Third Twin," around whom the mystery of genetic experimentation and identity unfolds.
-
E.
Don Logan
Don Logan is a volatile and menacing criminal character from the British film "Sexy Beast," best known for Ben Kingsley’s intense, Oscar-nominated performance.
- 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_69d88395e6c88190b22730f335107c14 |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b378dda48190ab81d75f2cfe3ab3 |
completed | April 18, 2026, 4:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00c2a4c2a881908d2797f6e61792d3 |
completed | May 10, 2026, 5:38 p.m. |
Created at: April 10, 2026, 5:24 a.m.