Triple
T16649170
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Personafilm |
E404556
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Persona |
E517170
|
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: Persona | Statement: [Personafilm, namedAfter, Persona]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Persona Context triple: [Personafilm, namedAfter, Persona]
-
A.
Persona
chosen
Persona is a 1966 Swedish psychological drama film by Ingmar Bergman, renowned for its experimental narrative, intense character study, and exploration of identity and reality.
-
B.
Pessoa
Pessoa is a common Portuguese surname most famously associated with the modernist writer Fernando Pessoa.
-
C.
Person
A Person is an individual human being who can be identified as a creator, contributor, or subject in bibliographic and information systems.
-
D.
Persons
Persons is the family name of Truman Streckfus Persons, better known as the American writer Truman Capote.
-
E.
Profile
Profile is a 2018 screenlife thriller film that unfolds entirely on a computer screen, following an undercover journalist infiltrating online extremist recruitment.
- 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_69d8838a41f08190b0c3f79c47df5078 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e37ad85ec881909dc6a434a363dab1 |
completed | April 18, 2026, 12:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0084c2a8448190a9f05bc7e1ce7a05 |
completed | May 10, 2026, 1:14 p.m. |
Created at: April 10, 2026, 5:18 a.m.