Triple
T5161001
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hodgkin lymphoma |
E116434
|
entity |
| Predicate | hasCellMarker |
P2130
|
FINISHED |
| Object | CD30 positivity in classical Hodgkin lymphoma |
—
|
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: CD30 positivity in classical Hodgkin lymphoma | Statement: [Hodgkin lymphoma, hasCellMarker, CD30 positivity in classical Hodgkin lymphoma]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCellMarker Context triple: [Hodgkin lymphoma, hasCellMarker, CD30 positivity in classical Hodgkin lymphoma]
-
A.
hasMarker
chosen
Indicates that one entity possesses, is associated with, or is identified by a specific marker.
-
B.
hasCells
Indicates that an entity contains, is composed of, or is associated with one or more cells.
-
C.
hasHistoricMarker
Indicates that something is associated with or identified by an official historic marker or plaque recognizing its historical significance.
-
D.
hasRow
Indicates that one entity contains, includes, or is associated with a specific row within a structured arrangement such as a table, grid, or dataset.
-
E.
hasStudentCells
Indicates that an entity contains, includes, or is associated with one or more cells designated as student cells.
- 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_69bd445edb3881909b93b34d260717fc |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd79073a54819080cd1e8de6fe906a |
completed | March 20, 2026, 4:42 p.m. |
| PD | Predicate disambiguation | batch_69bd77b36c008190b91011a9fa52b3d2 |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:44 p.m.