Triple
T12780584
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lord & Thomas |
E305495
|
entity |
| Predicate | client |
P27
|
FINISHED |
| Object | Pepsodent |
E985197
|
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: Pepsodent | Statement: [Lord & Thomas, client, Pepsodent]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pepsodent Context triple: [Lord & Thomas, client, Pepsodent]
-
A.
Pepsodent
chosen
Pepsodent is a long-established toothpaste brand known for its focus on cavity protection and oral hygiene, marketed globally by major consumer goods companies.
-
B.
Colgate
Colgate is a small village in West Sussex, England, known for its rural character and proximity to Horsham.
-
C.
Listerine
Listerine is a widely used antiseptic mouthwash brand known for its strong flavor and plaque- and germ-fighting oral care products.
-
D.
Colgate Thirteen
Colgate Thirteen is a renowned all-male a cappella group from Colgate University known for performing at high-profile events, including the national anthem at Super Bowl XIII.
-
E.
Crest
Crest is a well-known oral care brand, particularly recognized for its toothpastes and whitening products.
- 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_69d7bdf2b43c819098ae5aa68e61ea58 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e5a5680819095dcd491486d23e7 |
completed | April 10, 2026, 9:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f685030cbc8190856bf5254e231d25 |
completed | May 2, 2026, 11:13 p.m. |
Created at: April 9, 2026, 5:29 p.m.