Triple
T17688804
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | My Adidas |
E440965
|
entity |
| Predicate | Bside |
P15273
|
FINISHED |
| Object | Peter Piper |
—
|
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: Peter Piper | Statement: [My Adidas, Bside, Peter Piper]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peter Piper Context triple: [My Adidas, Bside, Peter Piper]
-
A.
Peter Piper
chosen
Peter Piper is a classic English-language tongue-twister character best known from the rhyme about picking a peck of pickled peppers.
-
B.
Peter Pond
Peter Pond was an 18th-century American fur trader, explorer, and cartographer who played a key role in opening up the Canadian Northwest for the fur trade.
-
C.
Pete
Pete is a common masculine given name, typically used as a familiar or informal form of the name Peter.
-
D.
Pete
Pete is a classic Disney cartoon villain, best known as Mickey Mouse’s burly, antagonistic foe in the Mickey Mouse franchise.
-
E.
Pete
Pete is a fictional character known as the son of Uncle Tom in Harriet Beecher Stowe’s anti-slavery novel "Uncle Tom’s Cabin."
- 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_69d8b9e940b081908b862bb0e6e89b0d |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4704a6bb4819083752285baf3b3ee |
completed | April 19, 2026, 6:03 a.m. |
Created at: April 10, 2026, 10:03 a.m.