Triple
T36599823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | B1 antigen |
E902886
|
entity |
| Predicate | relevantToField |
P6979
|
FINISHED |
| Object | hematopathology |
—
|
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: hematopathology | Statement: [B1 antigen, relevantToField, hematopathology]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relevantToField Context triple: [B1 antigen, relevantToField, hematopathology]
-
A.
relatedField
chosen
Indicates that one field, topic, or area of study is connected or relevant to another in subject matter or application.
-
B.
relatedWorkField
Indicates that one work is associated with or pertains to the same or a relevant field, discipline, or area of activity as another work.
-
C.
relevanceIn
Indicates that something is pertinent or applicable within a specified context, scope, or domain.
-
D.
relatedTo
Indicates a general, non-specific relationship or association exists between two entities.
-
E.
relevantToInstitution
Indicates that something has a meaningful connection, applicability, or significance to a particular institution.
- 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_69f76e66b7b88190848f7a3e1188915f |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7c371931c8190afb1d4dd5157f92c |
completed | May 3, 2026, 9:51 p.m. |
| PD | Predicate disambiguation | batch_69f7c1baf25c8190a78dd54a400d2c50 |
completed | May 3, 2026, 9:44 p.m. |
Created at: May 3, 2026, 4:11 p.m.