Triple
T180272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chinese Taipei |
E3857
|
entity |
| Predicate | termAdoptedIn |
P1362
|
FINISHED |
| Object | late 1970s |
—
|
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: late 1970s | Statement: [Chinese Taipei, termAdoptedIn, late 1970s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: termAdoptedIn Context triple: [Chinese Taipei, termAdoptedIn, late 1970s]
-
A.
adoptedBy
Indicates that an entity has been taken in and legally or formally accepted as a child, member, or responsibility by another entity.
-
B.
adoptedAt
Indicates the time or date at which an adoption event took place.
-
C.
dateAdopted
Indicates the date on which something (such as a policy, standard, or item) was formally accepted or put into official use.
-
D.
officialAdoptionYear
chosen
Indicates the year in which something (such as a policy, standard, symbol, or practice) was formally adopted or officially put into use.
-
E.
adopted
Indicates that one entity has legally taken another (often a child or animal) into its family or care as a permanent member.
- 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_69a25497e2f08190a040f8c6e1842643 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a25901a9188190b8f510bec8c8e7f2 |
completed | Feb. 28, 2026, 2:54 a.m. |
| PD | Predicate disambiguation | batch_69a2566ccc288190add5624ede96d82b |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:40 a.m.