Triple
T10037346
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MX |
E205203
|
entity |
| Predicate | featureScope |
P40827
|
FINISHED |
| Object | comfort features package |
—
|
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: comfort features package | Statement: [MX, featureScope, comfort features package]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featureScope Context triple: [MX, featureScope, comfort features package]
-
A.
featureType
Indicates the specific kind or category of feature that characterizes or distinguishes an entity.
-
B.
featureSet
chosen
Indicates that one entity is a collection or configuration of features associated with or applied to another entity.
-
C.
featuresGroup
Indicates that an entity includes or is associated with a specific group as one of its features or components.
-
D.
featuresSegment
Indicates that one entity includes or highlights a particular segment or portion of another entity as a notable part of it.
-
E.
focusFeature
Indicates that one entity is the primary or emphasized feature, aspect, or attribute being highlighted or concentrated on in relation to another.
- 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_69ca834f70e88190b2d74828b7767ec1 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cdcede428c8190ae115dc3425f9b0e |
completed | April 2, 2026, 2:05 a.m. |
| PD | Predicate disambiguation | batch_69cd4b8638508190b22acc65500ec7d6 |
completed | April 1, 2026, 4:44 p.m. |
Created at: March 30, 2026, 8:55 p.m.