Triple
T16853210
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LG Wing |
E409723
|
entity |
| Predicate | simConfiguration |
P101151
|
FINISHED |
| Object | single SIM |
—
|
LITERAL 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: single SIM | Statement: [LG Wing, simConfiguration, single SIM]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: simConfiguration Context triple: [LG Wing, simConfiguration, single SIM]
-
A.
simType
Indicates that two entities share the same or a comparable type, category, or classification.
-
B.
hasSIMConfiguration
chosen
Indicates that an entity is associated with or assigned a specific SIM card configuration or setup.
-
C.
signalsSetting
Indicates that one entity conveys or triggers information about a particular setting or configuration for another entity.
-
D.
simCount
Indicates the number of times two entities or items are considered similar according to a defined similarity measure.
-
E.
configuration
Indicates that one entity specifies or defines the arrangement, setup, or parameter settings under which another entity operates or is structured.
- F. None of above.
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_69d88395e6c88190b22730f335107c14 |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b37bbb80819086d844a313625cad |
completed | April 18, 2026, 4:38 p.m. |
| PD | Predicate disambiguation | batch_69e32b8cbb048190878a259cc5be960e |
completed | April 18, 2026, 6:58 a.m. |
Created at: April 10, 2026, 5:24 a.m.