Triple
T8429939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | David Furnish |
E199092
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Ogilvy & Mather |
E68896
|
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: Ogilvy & Mather | Statement: [David Furnish, employer, Ogilvy & Mather]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ogilvy & Mather Context triple: [David Furnish, employer, Ogilvy & Mather]
-
A.
Ogilvy & Mather
chosen
Ogilvy & Mather is a major global advertising agency known for its influential marketing campaigns and brand-building work for leading companies worldwide.
-
B.
Young & Rubicam
Young & Rubicam is a major global advertising agency known for its influential marketing campaigns and long-standing presence in the advertising industry.
-
C.
McCann Erickson
McCann Erickson is a global advertising agency network known for creating major international marketing campaigns for leading brands.
-
D.
Wunderman
Wunderman is a global marketing and advertising agency known for its data-driven, digital-first campaigns and customer relationship management services.
-
E.
BBDO
BBDO is a major global advertising agency network known for creating high-profile, award-winning campaigns for leading brands.
- 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_69ca8313c99081909a5c6d83b91de5b3 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbd1a2efa08190b92c75812003ffdb |
completed | March 31, 2026, 1:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf512d294c8190bed7e37991d237c1 |
completed | April 3, 2026, 5:33 a.m. |
Created at: March 30, 2026, 6:07 p.m.