Triple
T16008099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AJ Michalka |
E388269
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Amanda |
E230773
|
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: Amanda | Statement: [AJ Michalka, givenName, Amanda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amanda Context triple: [AJ Michalka, givenName, Amanda]
-
A.
Amanda
Amanda is the central character of the work "The Relapse," around whom the main events and conflicts of the story revolve.
-
B.
Amanda
chosen
Amanda is a feminine given name of Latin origin, meaning "worthy of love" or "lovable."
-
C.
Amanda
Amanda is the assumed identity of the main character Emily Thorne in the television series "Revenge."
-
D.
Amanda
Amanda is a central character in Margaret Atwood’s dystopian MaddAddam trilogy, known for her resilience and survival skills in a post-apocalyptic world.
-
E.
Amanda
Amanda is the central protagonist of the action-thriller film "Into the Blue," around whom the underwater treasure-hunting adventure revolves.
- 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_69d86dabcb7c8190b6a39d6831d2fa1b |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e15800e3608190bd3e1123ccc6c326 |
completed | April 16, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffcf22db3481909141ddef151d0341 |
completed | May 10, 2026, 12:19 a.m. |
Created at: April 10, 2026, 4:55 a.m.