Triple
T34939630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | grb Republike Hrvatske |
E1007678
|
entity |
| Predicate | rowsOfFields |
P36398
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [grb Republike Hrvatske, rowsOfFields, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rowsOfFields Context triple: [grb Republike Hrvatske, rowsOfFields, 5]
-
A.
rows
Indicates that one entity is arranged in a horizontal line or sequence relative to another, typically as part of a grid or tabular structure.
-
B.
hasNumberOfFields
chosen
Indicates the specific count of fields or distinct data elements that an entity possesses.
-
C.
numberOfColumns
Indicates the total count of vertical divisions (columns) associated with or contained in a given structure or dataset.
-
D.
numberOfSeatingRows
Indicates the total count of seating rows associated with an entity, such as a venue, vehicle, or seating area.
-
E.
fieldsPerFrame
Indicates the number of discrete fields that compose each video frame in an interlaced video signal.
- 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_69f76dc513fc819084a1ff52abbfa5bc |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f782c98fa08190870b68de2c1ff26a |
completed | May 3, 2026, 5:15 p.m. |
| PD | Predicate disambiguation | batch_69f781020cc4819088c40cb8589504e4 |
completed | May 3, 2026, 5:08 p.m. |
Created at: May 3, 2026, 4 p.m.