Triple
T19076589
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Max Engel |
E466920
|
entity |
| Predicate | hasRelative |
P367
|
FINISHED |
| Object | Tom Engel |
—
|
NE NERFINISHED |
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: Tom Engel | Statement: [Max Engel, hasRelative, Tom Engel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Engel Context triple: [Max Engel, hasRelative, Tom Engel]
-
A.
Tom Engel
chosen
Tom Engel is the young boy protagonist in the 2015 horror-comedy film "Krampus," whose loss of Christmas spirit triggers the arrival of the folkloric monster.
-
B.
Joe Engel
Joe Engel was a prominent baseball executive and promoter, often called the "Barnum of Baseball," known for his influential role in minor league baseball and association with the Chattanooga Lookouts.
-
C.
Don Ettlinger
Don Ettlinger was a screenwriter active in early 20th-century American cinema, known for adapting popular literary works for the screen.
-
D.
John Eisendrath
John Eisendrath is a television writer and producer best known for his work on series such as "The Blacklist" and "Alias."
-
E.
Bob Engelman
Bob Engelman is a film producer best known for working on major Hollywood movies, including the hit comedy "The Mask."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8dd04f4488190b1121cc53ef2bfd6 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e2e49c7c8190b6ce7b918086b23c |
completed | April 20, 2026, 8:25 a.m. |
Created at: April 10, 2026, 12:04 p.m.