Triple
T13933550
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Traffik |
E335050
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Luke Goss |
E111623
|
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: Luke Goss | Statement: [Traffik, starring, Luke Goss]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luke Goss Context triple: [Traffik, starring, Luke Goss]
-
A.
Luke Goss
chosen
Luke Goss is an English actor and former drummer best known for his roles in genre films such as "Blade II" and "Hellboy II: The Golden Army."
-
B.
Richard Gant
Richard Gant is an American character actor known for his roles in film and television, often portraying authoritative or tough-minded figures.
-
C.
Adam LaVorgna
Adam LaVorgna is an American actor best known for his roles in the television series "7th Heaven" and films such as "The Beautician and the Beast."
-
D.
Dean Winters
Dean Winters is an American actor best known for his roles on the HBO prison drama "Oz," the sitcom "30 Rock," and as the recurring "Mayhem" character in Allstate insurance commercials.
-
E.
Joel David Moore
Joel David Moore is an American actor and director best known for his roles in films like "Avatar" and the TV series "Bones."
- 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_69d81c5f739081908bc05b2461f54828 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2cf28df081908d897d7b9ec7939d |
completed | April 14, 2026, 12:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd54fa19c081908e6467ee7b79f02a |
completed | May 8, 2026, 3:14 a.m. |
Created at: April 9, 2026, 10:17 p.m.