Triple
T21664092
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flag of Georgia |
E534664
|
entity |
| Predicate | symbolCount |
P42152
|
FINISHED |
| Object | five crosses |
—
|
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: five crosses | Statement: [Flag of Georgia, symbolCount, five crosses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: symbolCount Context triple: [Flag of Georgia, symbolCount, five crosses]
-
A.
symbolNumber
Indicates a relationship where a specific numerical identifier is assigned to or associated with a particular symbol.
-
B.
signCount
Indicates the number of signs associated with, produced by, or present in relation to a given entity or context.
-
C.
symbolText
Indicates that a symbol is associated with or represented by a specific piece of text.
-
D.
numberOfCounts
chosen
Indicates the total quantity or tally of discrete occurrences, items, or instances associated with an entity or event.
-
E.
symbolType
Indicates the classification or category of a symbol based on its role, form, or function within a given system.
- 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_69e0c467e1f48190af2650b19175abc4 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef6c0a2a58819086db5b5c1c0f3371 |
completed | April 27, 2026, 2 p.m. |
| PD | Predicate disambiguation | batch_69e696826c3c81909270791e79760937 |
completed | April 20, 2026, 9:11 p.m. |
Created at: April 16, 2026, 6:36 p.m.