Triple
T18805420
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liz Tigelaar |
E459859
|
entity |
| Predicate | workedOn |
P3
|
FINISHED |
| Object | Dirty Sexy Money (TV series) |
—
|
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: Dirty Sexy Money (TV series) | Statement: [Liz Tigelaar, workedOn, Dirty Sexy Money (TV series)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dirty Sexy Money (TV series) Context triple: [Liz Tigelaar, workedOn, Dirty Sexy Money (TV series)]
-
A.
Dirty Sexy Money
chosen
Dirty Sexy Money is an American television drama series that follows a lawyer who becomes entangled in the scandals and secrets of a wealthy New York family.
-
B.
Dirty Money
Dirty Money is an American hip hop and R&B girl group formed by Sean "Diddy" Combs, known for blending soulful vocals with contemporary rap and dance production.
-
C.
Dirty Money
"Dirty Money" is a song by the Southern hip hop duo UGK, known for its gritty depiction of street life and hustling.
-
D.
Dirty Money
"Dirty Money" is a track by rapper Pusha T from his critically acclaimed 2006 album *Hell Hath No Fury*.
-
E.
Dirty Money
Dirty Money is a documentary television series that investigates corporate greed, corruption, and financial crime through in-depth case studies.
- 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_69d8d398c7d4819091cb2f7e48948aeb |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5a3d6d5e081909d8285e52cb753ba |
completed | April 20, 2026, 3:56 a.m. |
Created at: April 10, 2026, 11:53 a.m.