Triple
T27746482
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Windows Mixed Reality headsets |
E701996
|
entity |
| Predicate | includesModelsFrom |
P196619
|
FINISHED |
| Object | Acer |
—
|
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: Acer | Statement: [Windows Mixed Reality headsets, includesModelsFrom, Acer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesModelsFrom Context triple: [Windows Mixed Reality headsets, includesModelsFrom, Acer]
-
A.
usesModelsType
Indicates that one entity employs or relies on a specific type or category of models in its operation or behavior.
-
B.
includedWith
Indicates that one entity is provided or packaged together as part of another entity.
-
C.
isModelOf
Indicates that one entity serves as a representation or abstraction that captures the structure or behavior of another entity.
-
D.
includesDataFrom
Indicates that one entity’s data set contains or incorporates data originating from another entity.
-
E.
modelIn
Indicates that one entity serves as a representation or simulation of another entity.
- F. None of above. chosen
Provenance (4 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_69ef6a53c7388190899baa6daf42301c |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69fe5ec9028081909ae3d6fbe2f4cbbc |
completed | May 8, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69fe5e1d715881909fc516fafc707644 |
completed | May 8, 2026, 10:05 p.m. |
| PDg | Predicate description generation | batch_69fe5ec84910819094cb15269ace7c51 |
completed | May 8, 2026, 10:08 p.m. |
Created at: April 27, 2026, 4:16 p.m.