Triple
T22663197
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leo Burnett Worldwide |
E559715
|
entity |
| Predicate | hasNotableClient |
P7186
|
FINISHED |
| Object | Procter & Gamble |
—
|
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: Procter & Gamble | Statement: [Leo Burnett Worldwide, hasNotableClient, Procter & Gamble]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Procter & Gamble Context triple: [Leo Burnett Worldwide, hasNotableClient, Procter & Gamble]
-
A.
Procter & Gamble
chosen
Procter & Gamble is a multinational consumer goods corporation known for a wide range of household, personal care, and hygiene brands sold globally.
-
B.
Colgate-Palmolive
Colgate-Palmolive is a global consumer products company best known for its oral care, personal care, home care, and pet nutrition brands.
-
C.
Unilever
Unilever is a multinational consumer goods company known for its wide range of food, personal care, and household products sold globally.
-
D.
Henkel
Henkel is a German multinational chemical and consumer goods company best known for its brands in laundry, home care, and adhesives.
-
E.
Kimberly-Clark Corporation
Kimberly-Clark Corporation is a multinational personal care company best known for brands such as Kleenex, Huggies, and Scott paper products.
- 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_69e2454a158c819093b8e35f5045efb6 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f17660c0c88190bed9fa8f6517eec4 |
completed | April 29, 2026, 3:09 a.m. |
Created at: April 17, 2026, 3:08 p.m.