Triple
T7695114
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Handbuch der Naturgeschichte |
E174348
|
entity |
| Predicate | hasAuthorEmployer |
P78756
|
FINISHED |
| Object | University of Göttingen |
—
|
NE NERFINISHED |
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: University of Göttingen | Statement: [Handbuch der Naturgeschichte, hasAuthorEmployer, University of Göttingen]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAuthorEmployer Context triple: [Handbuch der Naturgeschichte, hasAuthorEmployer, University of Göttingen]
-
A.
hasAuthorRelationship
Indicates a relationship where one entity serves as the author or creator of another entity (such as a work, document, or resource).
-
B.
hasAuthor
Indicates that an entity is written or created by a specific author.
-
C.
hasFatherEmployer
Indicates that the employer of a person’s father is related to or associated with that person.
-
D.
namedForEmployer
Indicates that an entity is named after, or in honor of, its employer.
-
E.
isAssociatedWithProfessionOfBearer
Indicates that one entity is connected to, or involved with, the profession or occupational role held by 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_69c6995966348190939e6c37ba272c06 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c706d1f0208190bc5b695aa5736244 |
completed | March 27, 2026, 10:38 p.m. |
| PD | Predicate disambiguation | batch_69c70163dea88190ae729df50e63dfd7 |
completed | March 27, 2026, 10:15 p.m. |
| PDg | Predicate description generation | batch_69c706d0c1708190a3e74523997814b8 |
completed | March 27, 2026, 10:38 p.m. |
Created at: March 27, 2026, 4:02 p.m.