Triple
T2007407
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EAN-13 barcode system |
E43616
|
entity |
| Predicate | hasNumberOfDigits |
P11851
|
FINISHED |
| Object | 13 |
—
|
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: 13 | Statement: [EAN-13 barcode system, hasNumberOfDigits, 13]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfDigits Context triple: [EAN-13 barcode system, hasNumberOfDigits, 13]
-
A.
hasDigitCount
chosen
Indicates that one entity has a total number of digits equal to the value specified by the other entity.
-
B.
hasDigitSet
Indicates that an entity is associated with a specific set or collection of digits.
-
C.
hasNumberOfLetters
Indicates a relationship where an entity is associated with the count of letters it contains.
-
D.
hasCheckDigit
Indicates that an identifier or code includes a calculated check digit used to verify its correctness or integrity.
-
E.
hasApproximateNumberOfLetters
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
- 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_69a88716e9f08190946313fdc949e3cf |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb8999e108190a07daa01452a5dab |
completed | March 7, 2026, 5:33 a.m. |
| PD | Predicate disambiguation | batch_69abb79e63c08190982c8b44a557266f |
completed | March 7, 2026, 5:29 a.m. |
Created at: March 4, 2026, 7:37 p.m.