Triple
T18069094
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cathy Engelbert |
E432372
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Deloitte US |
—
|
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: Deloitte US | Statement: [Cathy Engelbert, employer, Deloitte US]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Deloitte US Context triple: [Cathy Engelbert, employer, Deloitte US]
-
A.
Deloitte
chosen
Deloitte is one of the world’s largest professional services firms, providing audit, consulting, tax, and advisory services to clients globally.
-
B.
Ernst & Young
Ernst & Young is a global professional services firm, known as one of the "Big Four" accounting organizations, providing audit, tax, consulting, and advisory services to clients worldwide.
-
C.
PricewaterhouseCoopers
PricewaterhouseCoopers (PwC) is one of the world’s largest professional services networks, providing audit, tax, and consulting services to clients across a wide range of industries.
-
D.
KPMG
KPMG is one of the world’s largest professional services firms, providing audit, tax, and advisory services to clients across a wide range of industries.
-
E.
Accenture
Accenture is a global professional services company specializing in consulting, technology, and outsourcing solutions for businesses and governments worldwide.
- 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_69d8b9070cac81909fa9473fb1c3f1c7 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4ccebff748190b41d2edd93994c67 |
completed | April 19, 2026, 12:39 p.m. |
Created at: April 10, 2026, 10:26 a.m.