Triple
T6120739
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zimbabwe Human Rights Commission |
E136475
|
entity |
| Predicate | hasLegalPersonality |
P3414
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Zimbabwe Human Rights Commission, hasLegalPersonality, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLegalPersonality Context triple: [Zimbabwe Human Rights Commission, hasLegalPersonality, yes]
-
A.
legalPersonality
chosen
Indicates that an entity possesses recognized legal status, enabling it to hold rights, bear obligations, and act as a subject under the law.
-
B.
usesLegalEntity
Indicates that one entity makes use of, operates through, or conducts activities via a particular legal entity (such as a company, organization, or other legally recognized body).
-
C.
hasLegalStatus
Indicates that an entity possesses a particular legal classification, recognition, or standing under law.
-
D.
hasLegalSubject
Indicates that an entity serves as the legal subject (e.g., rights-holder or obligated party) in a legal relationship or context.
-
E.
belongsToCompanyLegalForm
Indicates that an entity is associated with or classified under a specific legal form of a company.
- 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_69c0089f851c81909e5e189a617dcff6 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05bef8dc08190b917ad7209188c62 |
completed | March 22, 2026, 9:15 p.m. |
| PD | Predicate disambiguation | batch_69c049f9ab3c81909c8ab6466f6a2935 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:14 p.m.