Triple
T370511
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cancer Immunology Research |
E8257
|
entity |
| Predicate | fieldIntersection |
P11740
|
FINISHED |
| Object | immunology and cancer biology |
—
|
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: immunology and cancer biology | Statement: [Cancer Immunology Research, fieldIntersection, immunology and cancer biology]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fieldIntersection Context triple: [Cancer Immunology Research, fieldIntersection, immunology and cancer biology]
-
A.
crossesSectionOf
Indicates that one entity passes through or over a specific segment or portion of another entity.
-
B.
crossesBetween
Indicates that one entity passes from one side of a second entity to the other, traversing the space between two reference points or boundaries associated with that second entity.
-
C.
fieldCovered
Indicates that a specified field or area is physically or functionally covered by some material, object, or condition.
-
D.
crossedBy
Indicates that one entity (typically a path, line, or boundary) is intersected or traversed by another entity.
-
E.
fieldPattern
Indicates a recurring or structured arrangement or configuration present within a field or area.
- F. None of above. chosen
Provenance (4 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_69a2e7f2ec648190b42bc7db424f8109 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ebff472881909fad81d597425ea6 |
completed | Feb. 28, 2026, 1:22 p.m. |
| PD | Predicate disambiguation | batch_69a2e960d880819084b3df4e5137a1e2 |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2ea0b23ec8190bef9d593162388a4 |
completed | Feb. 28, 2026, 1:13 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.