Triple
T9325927
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EAN-8 barcode system |
E224384
|
entity |
| Predicate | hasEncodingType |
P55911
|
FINISHED |
| Object | numeric-only |
—
|
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: numeric-only | Statement: [EAN-8 barcode system, hasEncodingType, numeric-only]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEncodingType Context triple: [EAN-8 barcode system, hasEncodingType, numeric-only]
-
A.
hasMIMECharsetName
Indicates that an entity (such as a character encoding) is associated with a specific MIME charset name used in internet protocols.
-
B.
hasContentType
Indicates that an entity is associated with or classified by a specific type of content.
-
C.
dataEncodingMethod
Indicates the specific technique or format used to encode data for storage, transmission, or processing.
-
D.
hasTypeCode
Indicates that an entity is associated with a specific type classification represented by a code.
-
E.
textEncoderType
chosen
Indicates the specific kind or configuration of encoder used to process or represent text in a system or model.
- 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_69ca8427a0c08190b749831d5ea98f02 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd36f88e988190bb896a3d7c3c723c |
completed | April 1, 2026, 3:17 p.m. |
| PD | Predicate disambiguation | batch_69cc7a643924819097f01144734901cf |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:39 p.m.