Triple
T10067102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lawrence Tynes |
E213127
|
entity |
| Predicate | sufferedCondition |
P91920
|
FINISHED |
| Object | MRSA infection |
—
|
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: MRSA infection | Statement: [Lawrence Tynes, sufferedCondition, MRSA infection]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sufferedCondition Context triple: [Lawrence Tynes, sufferedCondition, MRSA infection]
-
A.
conditions
Indicates that one entity specifies or imposes requirements, constraints, or circumstances that must be satisfied or hold true for another entity or situation.
-
B.
sufferedDamageTo
Indicates that one entity has experienced harm, loss, or deterioration affecting another entity or one of its parts.
-
C.
curedWith
Indicates that one entity is treated or healed by using another entity as the remedy or therapeutic method.
-
D.
hasTypicalConditions
Indicates that something is associated with conditions or circumstances that are commonly or normally present for it.
-
E.
regardsSufferingAs
Indicates how an entity perceives, evaluates, or emotionally responds to the suffering of another entity.
- 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_69ca83977128819084084eb7d1d8c52a |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdcff63a4c8190bb08a0428aafa189 |
completed | April 2, 2026, 2:09 a.m. |
| PD | Predicate disambiguation | batch_69cd4b92573481909389bc6148ae7ea8 |
completed | April 1, 2026, 4:45 p.m. |
| PDg | Predicate description generation | batch_69cd4f8d9b888190b8067bd916dae773 |
completed | April 1, 2026, 5:02 p.m. |
Created at: March 30, 2026, 8:58 p.m.