Triple
T9000946
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NEC aperture grille CRTs |
E215036
|
entity |
| Predicate | imageCharacteristic |
P52703
|
FINISHED |
| Object | sharp image |
—
|
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: sharp image | Statement: [NEC aperture grille CRTs, imageCharacteristic, sharp image]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: imageCharacteristic Context triple: [NEC aperture grille CRTs, imageCharacteristic, sharp image]
-
A.
dataCharacteristic
Indicates that one entity specifies a property, attribute, or feature that characterizes a given piece of data.
-
B.
visualFeature
Indicates a relationship where one entity possesses or exhibits a particular visual characteristic or attribute of another entity.
-
C.
mediaCharacterization
Indicates how an entity is portrayed, described, or framed by media sources in terms of attributes, tone, or narrative.
-
D.
imageQuality
chosen
Indicates the assessed level or degree of visual clarity, detail, and overall fidelity of an image.
-
E.
catalogCharacteristic
Indicates that a catalog has a specific characteristic or attribute associated with it.
- 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.