Triple
T21830785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siri Mullinix |
E538988
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Siri |
—
|
NE NERFINISHED |
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: Siri | Statement: [Siri Mullinix, givenName, Siri]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Siri Context triple: [Siri Mullinix, givenName, Siri]
-
A.
Siri
chosen
Siri is Apple's intelligent voice-controlled virtual assistant that performs tasks, answers questions, and controls devices across the Apple ecosystem.
-
B.
Alexa
Alexa is a feminine given name commonly used in English-speaking countries, often as a shortened form of Alexandra.
-
C.
Alexa
Alexa is Amazon’s cloud-based virtual assistant that uses voice interaction to control smart devices, answer questions, and perform a variety of digital tasks.
-
D.
Bixby
Bixby is Samsung's proprietary virtual assistant designed to enable voice control, smart device integration, and contextual assistance across the company's ecosystem of products.
-
E.
Bixby
Bixby is an English-language surname borne by various notable individuals in entertainment, sports, and other fields.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c475cda88190987d08f23caebdc1 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69f0913554508190b81347f01d3903e8 |
completed | April 28, 2026, 10:51 a.m. |
Created at: April 16, 2026, 6:54 p.m.