Triple
T19376796
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | He Jiankui |
E484689
|
entity |
| Predicate | professionalConsequence |
P123056
|
FINISHED |
| Object | dismissed from Southern University of Science and Technology |
—
|
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: dismissed from Southern University of Science and Technology | Statement: [He Jiankui, professionalConsequence, dismissed from Southern University of Science and Technology]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: professionalConsequence Context triple: [He Jiankui, professionalConsequence, dismissed from Southern University of Science and Technology]
-
A.
professionalOutcome
chosen
Indicates the resulting professional status, achievement, or consequence that arises from a person’s work-related actions, experiences, or decisions.
-
B.
professional
Indicates that one entity has a formal, occupation-related role, service, or expertise in relation to another entity.
-
C.
professionalWins
Indicates that one entity has achieved a certain number of victories or successes in a professional context, such as in a career, competition, or formal domain.
-
D.
professionalSince
Indicates the point in time when an entity began its professional activity or career in a given role or field.
-
E.
professionalCategory
Indicates the classification of an entity according to its professional field, role, or occupational domain.
- 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_69d8e8d460d88190abf0591c5c9d2b0c |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e61a5cfbf48190ac60e3ffa6baa263 |
completed | April 20, 2026, 12:21 p.m. |
| PD | Predicate disambiguation | batch_69e4fd54f8e48190956e73dd8969164a |
completed | April 19, 2026, 4:05 p.m. |
Created at: April 10, 2026, 1:35 p.m.