Triple
T13969134
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peyton Page |
E336006
|
entity |
| Predicate | hasGivenName |
P17
|
FINISHED |
| Object | Peyton |
E132632
|
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: Peyton | Statement: [Peyton Page, hasGivenName, Peyton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peyton Context triple: [Peyton Page, hasGivenName, Peyton]
-
A.
Peyton
chosen
Peyton is a given name most famously borne by Peyton Randolph, the first president of the Continental Congress in early American history.
-
B.
Peyton Page
Peyton Page is a relatively uncommon personal name that may refer to various individuals rather than a single widely recognized public figure.
-
C.
Peyton Van Den Broeck
Peyton Van Den Broeck is known as the spouse of Dutch Van Den Broeck, a central character in the crime novel and film "Random Hearts."
-
D.
Peyton Westlake
Peyton Westlake is a disfigured scientist-turned-vigilante who uses synthetic skin and brutal methods to seek revenge in the Darkman film series.
-
E.
Peyton List
Peyton List is an American actress and model best known for her roles on Disney Channel series such as "Jessie" and "Bunk'd."
- 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_69d81c61f3508190aaf2ca0dc0002c59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e8daeac8190aadd4b3b60222482 |
completed | April 14, 2026, 12:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fba1dc838c8190bbcfefd69ea29965 |
completed | May 6, 2026, 8:17 p.m. |
Created at: April 9, 2026, 10:18 p.m.