Triple
T30382234
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Huawei P20 Lite |
E772861
|
entity |
| Predicate | hasAmbientLightSensor |
P17204
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Huawei P20 Lite, hasAmbientLightSensor, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAmbientLightSensor Context triple: [Huawei P20 Lite, hasAmbientLightSensor, yes]
-
A.
hasProximitySensor
Indicates that an entity is equipped with a sensor capable of detecting nearby objects or measuring its distance to them.
-
B.
hasTorch
Indicates that one entity possesses or is currently holding a torch in relation to another context or entity.
-
C.
hasLumen
Indicates that one entity possesses or is characterized by a specific luminous intensity or light output measured in lumens.
-
D.
sensitivityToLight
Indicates a relationship where an entity reacts adversely or more strongly than normal when exposed to light.
-
E.
hasSensor
chosen
Indicates that one entity is equipped with, contains, or uses a particular sensor.
- 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_69f2248e3444819081b05712dc6873de |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6851aeb948190b674200cf6bf51f9 |
completed | May 2, 2026, 11:13 p.m. |
| PD | Predicate disambiguation | batch_69f678d019fc8190913662cd2f87b857 |
completed | May 2, 2026, 10:21 p.m. |
Created at: April 29, 2026, 8 p.m.