Triple
T7541083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Argentine coat of arms |
E178273
|
entity |
| Predicate | ribbonMeaning |
P77717
|
FINISHED |
| Object | national colors of Argentina |
—
|
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: national colors of Argentina | Statement: [Argentine coat of arms, ribbonMeaning, national colors of Argentina]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ribbonMeaning Context triple: [Argentine coat of arms, ribbonMeaning, national colors of Argentina]
-
A.
starMeaning
Indicates that one entity represents or conveys the symbolic or interpretive significance of a star associated with another entity.
-
B.
ribbonName
Indicates the specific name or label assigned to a ribbon within a system or context.
-
C.
badgeSymbolMeaning
Indicates that a badge’s symbol represents or conveys a particular meaning or significance.
-
D.
ribbonType
Indicates the specific kind or category of ribbon associated with an entity.
-
E.
letterMeaning
Indicates that a particular letter conveys a specific meaning, interpretation, or semantic content.
- F. None of above. chosen
Provenance (4 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_69c69f2be3888190a6667a27f8f195e9 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f8750f80819088ddfb7a5580b5df |
completed | March 27, 2026, 9:36 p.m. |
| PD | Predicate disambiguation | batch_69c6f4d8eedc81908c1ae421e0e63798 |
completed | March 27, 2026, 9:21 p.m. |
| PDg | Predicate description generation | batch_69c6f5cea2748190afd607ef93e8d66c |
completed | March 27, 2026, 9:25 p.m. |
Created at: March 27, 2026, 3:48 p.m.