Triple
T7478144
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Argentine flag |
E176681
|
entity |
| Predicate | officiallyStandardized |
P7508
|
FINISHED |
| Object | 1985 |
—
|
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: 1985 | Statement: [Argentine flag, officiallyStandardized, 1985]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officiallyStandardized Context triple: [Argentine flag, officiallyStandardized, 1985]
-
A.
standardizedIn
chosen
Indicates that something has been formally defined, regulated, or made uniform within a particular standard, framework, or jurisdiction.
-
B.
standardizedBy
Indicates that one entity defines, regulates, or formalizes the standards or specifications by which another entity is created, measured, or operated.
-
C.
firstStandardized
Indicates that an entity is the earliest or primary instance to which a standard or uniform specification has been first applied among comparable entities.
-
D.
standardizedFor
Indicates that something has been adjusted or converted to conform to a common standard, format, or reference so it can be consistently compared or used.
-
E.
wasGraduallyStandardizedIn
Indicates that something became standardized or uniform within a particular context through a gradual, step-by-step process over time.
- 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_69c69f236ce08190a04d7679f03b29b2 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f4f0088c8190880770ac31e5b7a7 |
completed | March 27, 2026, 9:21 p.m. |
| PD | Predicate disambiguation | batch_69c6f03d967081908a8e696ff9693b90 |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:42 p.m.