Triple
T16866208
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Welles |
E410044
|
entity |
| Predicate | appearsIn |
P795
|
FINISHED |
| Object | 8MM |
E86275
|
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: 8MM | Statement: [Tom Welles, appearsIn, 8MM]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 8MM Context triple: [Tom Welles, appearsIn, 8MM]
-
A.
8MM
chosen
8MM is a 1999 neo-noir crime thriller film starring Nicolas Cage as a private investigator drawn into the dark underworld of illegal pornography.
-
B.
8MM 2
8MM 2 is a direct-to-video crime thriller film that continues the dark, investigative themes of the original 8MM with a new cast and storyline.
-
C.
MM38
MM38 is a ship-launched variant of the French-made Exocet anti-ship missile, designed for engaging surface vessels at sea.
-
D.
BMM
BMM (Business Motivation Model) is a standardized framework by the Object Management Group for modeling and analyzing an organization’s business plans, motivations, and governance.
-
E.
BB-64
BB-64 is the hull number of USS Wisconsin, an Iowa-class battleship that served in the U.S. Navy during World War II, the Korean War, and the Gulf War.
- 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_69e3b5088f208190abfe937633ebe3fe |
completed | April 18, 2026, 4:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00bb2a62f48190b9174e85e6e1b7bd |
completed | May 10, 2026, 5:06 p.m. |
Created at: April 10, 2026, 5:24 a.m.