Triple
T3193810
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cameron |
E66887
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Camryn |
E191487
|
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: Camryn | Statement: [Cameron, hasVariant, Camryn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Camryn Context triple: [Cameron, hasVariant, Camryn]
-
A.
Skylar
Skylar is a compassionate and intelligent Harvard student who becomes Will Hunting’s love interest in the film "Good Will Hunting."
-
B.
Ryann
chosen
Ryann is a given name, typically used as a modern variant of the name Ryan.
-
C.
Jenna
Jenna is a common feminine given name, often used as a diminutive or variant of Jennifer.
-
D.
Brenna Harding
Brenna Harding is an Australian actress best known for her roles in the TV series "Puberty Blues" and the "Arkangel" episode of "Black Mirror."
-
E.
Carley Knox
Carley Knox is a sports executive best known for her leadership role in the WNBA’s Minnesota Lynx organization.
- 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_69ad8588ba18819086a10951c32ecb80 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada714dbcc8190a7ad21b3c957f151 |
completed | March 8, 2026, 4:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b24bae63688190b2a1a1dba8b2cffa |
completed | March 12, 2026, 5:14 a.m. |
Created at: March 8, 2026, 3:07 p.m.