Triple
T772948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Augsburger Panther |
E16320
|
entity |
| Predicate | hasProfessionalStatus |
P19008
|
FINISHED |
| Object | professional |
—
|
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: professional | Statement: [Augsburger Panther, hasProfessionalStatus, professional]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProfessionalStatus Context triple: [Augsburger Panther, hasProfessionalStatus, professional]
-
A.
hasLegalStatus
Indicates that an entity possesses a particular legal classification, recognition, or standing under law.
-
B.
hadOccupationStatusUntil
Indicates that an entity held a particular occupational status up to, but not necessarily beyond, a specified point in time.
-
C.
hasNotableBearerOccupation
Indicates that an entity is associated with a notable person who holds a specific occupation.
-
D.
hasInfluentialStatus
Indicates that an entity holds a position, role, or condition that gives it significant influence or impact over others or over outcomes.
-
E.
hasWorkedIn
Indicates that a person has been employed or has performed work within a particular organization, location, or domain for some period of time.
- 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_69a49369a0848190af883934cee3db4c |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a72eda6c81908205ae5a1e05cc20 |
completed | March 1, 2026, 8:53 p.m. |
| PD | Predicate disambiguation | batch_69a4a508c42c8190850a0ac7844a3ea9 |
completed | March 1, 2026, 8:43 p.m. |
| PDg | Predicate description generation | batch_69a4a5a35c68819082429755c046e9a7 |
completed | March 1, 2026, 8:46 p.m. |
Created at: March 1, 2026, 7:37 p.m.