Triple
T16572610
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bluth |
E402623
|
entity |
| Predicate | hasLegalIssueInPlot |
P4511
|
FINISHED |
| Object | real estate fraud |
—
|
LITERAL 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: real estate fraud | Statement: [Bluth, hasLegalIssueInPlot, real estate fraud]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLegalIssueInPlot Context triple: [Bluth, hasLegalIssueInPlot, real estate fraud]
-
A.
hasLegalIssue
chosen
Indicates that an entity is involved in, associated with, or subject to a legal problem, dispute, or proceeding.
-
B.
mainLegalIssue
Indicates the primary legal question or dispute that is central to a case or legal matter.
-
C.
hasLegalRelevanceIn
Indicates that something is legally significant, applicable, or has consequences within a specified legal context, case, or jurisdiction.
-
D.
hasLegalProceeding
Indicates that there is a formal legal action, case, or proceeding involving the related entities.
-
E.
hasPoliticalPlot
Indicates that the subject work contains a storyline or narrative element centered on politics, political events, or political power struggles.
- F. None of above.
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_69d8838648088190acf97ef11fc3f61b |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e35959f1748190934b263d6bc94143 |
completed | April 18, 2026, 10:13 a.m. |
| PD | Predicate disambiguation | batch_69e296a47b7481909d9958158510c806 |
completed | April 17, 2026, 8:23 p.m. |
Created at: April 10, 2026, 5:16 a.m.