Triple
T21593673
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flag of Iran |
E532841
|
entity |
| Predicate | featureCount |
P46645
|
FINISHED |
| Object | three horizontal stripes |
—
|
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: three horizontal stripes | Statement: [Flag of Iran, featureCount, three horizontal stripes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featureCount Context triple: [Flag of Iran, featureCount, three horizontal stripes]
-
A.
hasNumberOfFeaturesDescribed
Indicates that an entity is associated with a specific count of its features that have been described.
-
B.
featuresSample
chosen
Indicates that an entity includes or presents a particular sample as one of its components or examples.
-
C.
featuresSampling
Indicates that an entity includes or employs a particular method or configuration for sampling.
-
D.
featureSet
Indicates that one entity is a collection or configuration of features associated with or applied to another entity.
-
E.
numberOfFaces
Indicates the relationship that specifies how many faces a given object or entity has.
- 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_69e0c46251648190876f0427cf2d321b |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eefadf6e608190b42b26ea22c76ec6 |
completed | April 27, 2026, 5:57 a.m. |
| PD | Predicate disambiguation | batch_69e632109d048190b4ac3f14fe48d1a0 |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:32 p.m.