Triple
T9792030
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Romanized Popular Alphabet |
E237627
|
entity |
| Predicate | adoptionPeriod |
P84186
|
FINISHED |
| Object | mid-20th century |
—
|
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: mid-20th century | Statement: [Romanized Popular Alphabet, adoptionPeriod, mid-20th century]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: adoptionPeriod Context triple: [Romanized Popular Alphabet, adoptionPeriod, mid-20th century]
-
A.
timePeriodOfAdoption
chosen
Indicates the specific time period during which something was adopted or came into use.
-
B.
adoptionPeak
Indicates the point in time at which the rate or level of adoption of something reaches its highest value.
-
C.
adoptedAfter
Indicates that one entity was adopted at a later time than another entity.
-
D.
readoptedOn
Indicates the date or time at which something that was previously adopted is formally adopted again.
-
E.
adoptionFrequency
Indicates how often an entity adopts or takes on another entity, such as a practice, item, or individual, over a given period.
- 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_69ca84dc04488190b9c91193976c0960 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda3456bac819097afb20ce9081dd7 |
completed | April 1, 2026, 10:59 p.m. |
| PD | Predicate disambiguation | batch_69cd03da45a88190b71b1be3354c15a6 |
completed | April 1, 2026, 11:39 a.m. |
Created at: March 30, 2026, 8:28 p.m.