Triple
T19973867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cornelis Springer |
E493636
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Springer |
—
|
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: Springer | Statement: [Cornelis Springer, familyName, Springer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Springer Context triple: [Cornelis Springer, familyName, Springer]
-
A.
Springer
chosen
Springer is a surname most prominently associated in contemporary sports with George Springer, an American professional baseball outfielder and World Series MVP.
-
B.
Springer
Springer is an Autobot action hero from the Transformers franchise, known for his green triple-changing form and prominent role in the 1986 animated film.
-
C.
Springer
Springer is a major global academic publishing company known for its extensive catalog of scientific, technical, and medical books and journals.
-
D.
Birkhäuser
Birkhäuser is a Swiss-based academic publishing house renowned for its high-quality books and journals in architecture, design, and the natural sciences.
-
E.
Mouton de Gruyter
Mouton de Gruyter is an academic publishing house known for its specialized works in linguistics and related fields.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da626a67648190af9653832a3aeced |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e65bcb72048190aedb4f085ace0493 |
completed | April 20, 2026, 5 p.m. |
Created at: April 11, 2026, 3:17 p.m.