Triple
T14461036
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cayden Boyd |
E358583
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Cayden |
E476422
|
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: Cayden | Statement: [Cayden Boyd, givenName, Cayden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cayden Context triple: [Cayden Boyd, givenName, Cayden]
-
A.
Cayden
chosen
Cayden is a modern unisex given name, often considered a variant of Caden and popular in English-speaking countries.
-
B.
Cade
Cade is a surname most notably associated with Brittney Cade, known for her connection to the music and entertainment world.
-
C.
Cayden Boyd
Cayden Boyd is an American actor best known for his childhood role as Max in the fantasy film "The Adventures of Sharkboy and Lavagirl in 3-D."
-
D.
Cade Yeager
Cade Yeager is a human inventor and mechanic who becomes a key ally to the Autobots in the later live-action Transformers films.
-
E.
Skylar
Skylar is a compassionate and intelligent Harvard student who becomes Will Hunting’s love interest in the film "Good Will Hunting."
- 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_69d82794dfa081909b9134ad2e32244b |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de91abc1008190a19de4f8f0112c9d |
completed | April 14, 2026, 7:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd6d890be88190afe61dde0d1e75a8 |
completed | May 8, 2026, 4:58 a.m. |
Created at: April 10, 2026, 1:19 a.m.