Triple
T9000931
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mitsubishi Diamondtron |
E215035
|
entity |
| Predicate | maskType |
P65729
|
FINISHED |
| Object | vertical aperture grille |
—
|
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: vertical aperture grille | Statement: [Mitsubishi Diamondtron, maskType, vertical aperture grille]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maskType Context triple: [Mitsubishi Diamondtron, maskType, vertical aperture grille]
-
A.
typicalMaskType
chosen
Indicates the kind or category of mask that is most commonly or characteristically used or associated with an entity.
-
B.
maskColor
Indicates the color attribute associated with a mask.
-
C.
traditionalMaskType
Indicates the specific kind or category of traditional mask associated with an entity.
-
D.
mayMask
Indicates that one entity is permitted or able to conceal, obscure, or hide another entity or its properties.
-
E.
faceType
Indicates the specific shape or structural category of a face that an entity possesses or is characterized by.
- 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_69ca83a12d648190b1e4fe11e8a31890 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6954bb1881908d004a26ba7fe360 |
completed | April 1, 2026, 12:39 a.m. |
| PD | Predicate disambiguation | batch_69cc5edd6cb48190b4fc6d6ca0418056 |
completed | March 31, 2026, 11:55 p.m. |
Created at: March 30, 2026, 7:05 p.m.