Triple
T20859195
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arthur Andersen |
E513569
|
entity |
| Predicate | effectOfScandal |
P142134
|
FINISHED |
| Object | Loss of clients |
—
|
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: Loss of clients | Statement: [Arthur Andersen, effectOfScandal, Loss of clients]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOfScandal Context triple: [Arthur Andersen, effectOfScandal, Loss of clients]
-
A.
associatedScandal
Indicates a relationship where an entity is linked to, involved in, or notably connected with a particular scandal.
-
B.
timeOfMajorScandal
Indicates the specific time period during which a major scandal involving the entity occurred.
-
C.
disgracedFor
Indicates that an entity has lost honor, respect, or status specifically because of the associated reason, action, or circumstance.
-
D.
causeOfReputation
Indicates that one entity is the reason or source for another entity’s reputation.
-
E.
politicalRepercussion
Indicates that an action, event, or decision leads to consequences or fallout within a political context, such as shifts in power, public opinion, or policy.
- F. None of above. chosen
Provenance (4 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_69e0b4f5b01081909452f654d2fc3f50 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c3aabef4819098f0fd24dcc27dbd |
completed | April 21, 2026, 12:24 a.m. |
| PD | Predicate disambiguation | batch_69e5c9a593f481908beb457c29f1ce73 |
completed | April 20, 2026, 6:37 a.m. |
| PDg | Predicate description generation | batch_69e5d53c4d6881909b4d0a716fa5ed4a |
completed | April 20, 2026, 7:26 a.m. |
Created at: April 16, 2026, 12:44 p.m.