Triple
T34103482
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beneke Fabricators |
E874636
|
entity |
| Predicate | hasBusinessProblem |
P60122
|
FINISHED |
| Object | unpaid back taxes |
—
|
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: unpaid back taxes | Statement: [Beneke Fabricators, hasBusinessProblem, unpaid back taxes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBusinessProblem Context triple: [Beneke Fabricators, hasBusinessProblem, unpaid back taxes]
-
A.
hasBusiness
Indicates that one entity owns, operates, or is formally associated with a business entity.
-
B.
businessChallenge
chosen
Indicates that an entity faces, presents, or is associated with a problem, obstacle, or difficulty within a business context.
-
C.
hasBusinessNode
Indicates that an entity is associated with, linked to, or represented by a specific business-related node within a business structure or network.
-
D.
hasIssueWith
Indicates that one entity experiences a problem, conflict, or concern related to another entity.
-
E.
hasBusinessSection
Indicates that an entity (such as a publication or website) includes a dedicated section focused on business-related content or topics.
- 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_69f349a80d4481908527317d43f5c579 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fb563aec448190875410fb1a3ed624 |
completed | May 6, 2026, 2:54 p.m. |
| PD | Predicate disambiguation | batch_69fb35b9ede881908aaae93a215525df |
completed | May 6, 2026, 12:36 p.m. |
Created at: May 1, 2026, 1:53 a.m.