Triple
T5699347
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Havoc |
E125618
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Blood Money |
E433021
|
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: Blood Money | Statement: [Havoc, notableWork, Blood Money]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Blood Money Context triple: [Havoc, notableWork, Blood Money]
-
A.
Blood Money
chosen
Blood Money is a dark, theatrical 2002 album by Tom Waits, known for its cabaret-style songs and lyrics drawn from the play "Woyzeck."
-
B.
The Big Money
The Big Money is a 1936 novel by John Dos Passos, best known as the third volume of his U.S.A. trilogy, which critiques American capitalism and society in the early 20th century through experimental narrative techniques.
-
C.
Dirty Money
"Dirty Money" is a track by rapper Pusha T from his critically acclaimed 2006 album *Hell Hath No Fury*.
-
D.
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.
-
E.
A Truck Full of Money
A Truck Full of Money is a nonfiction book by Tracy Kidder that profiles tech entrepreneur Paul English and explores the culture, psychology, and volatility of startup success.
- 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_69c0082c96988190b3a6a201edce472a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0241030408190be774a5d2ca6e999 |
completed | March 22, 2026, 5:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c05a5c89b88190a397c6b1dcb9c3e8 |
completed | March 22, 2026, 9:08 p.m. |
Created at: March 22, 2026, 3:45 p.m.