Triple
T647624
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. Hermann Gottlieb |
E11275
|
entity |
| Predicate | physicalCondition |
P3816
|
FINISHED |
| Object | chronic leg impairment |
—
|
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: chronic leg impairment | Statement: [Dr. Hermann Gottlieb, physicalCondition, chronic leg impairment]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: physicalCondition Context triple: [Dr. Hermann Gottlieb, physicalCondition, chronic leg impairment]
-
A.
involvedPhysicalEffect
Indicates that one entity participates in causing, experiencing, or mediating a physical effect on another entity or the environment.
-
B.
healthProxy
Indicates that one entity is authorized to make health-related or medical decisions on behalf of another entity.
-
C.
eligibleBody
Indicates that an entity qualifies as an appropriate or permitted body (e.g., organization or institution) to participate in or be subject to a specified relationship or action.
-
D.
strength
Indicates the degree of power, intensity, or effectiveness with which an entity can act on, influence, or withstand another entity or force.
-
E.
hasInjuries
chosen
Indicates that an entity has sustained one or more physical or bodily injuries.
- 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_69a493266a2881909daf4c40f719dee8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f1cb24481909d3b41a56b29dee9 |
completed | March 1, 2026, 8:18 p.m. |
| PD | Predicate disambiguation | batch_69a49d0c0dcc8190849211d45489a5a7 |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:36 p.m.