Triple
T35909877
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kate Schatz |
E1038575
|
entity |
| Predicate | hasCollaborator |
P10645
|
FINISHED |
| Object |
Miriam Klein Stahl
Miriam Klein Stahl is an American artist and illustrator best known for her bold, cut-paper portraits in feminist and social justice–themed books created in collaboration with author Kate Schatz.
|
E2172354
|
NE 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: Miriam Klein Stahl | Statement: [Kate Schatz, hasCollaborator, Miriam Klein Stahl]
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Miriam Klein Stahl Triple: [Kate Schatz, hasCollaborator, Miriam Klein Stahl]
Generated description
Miriam Klein Stahl is an American artist and illustrator best known for her bold, cut-paper portraits in feminist and social justice–themed books created in collaboration with author Kate Schatz.
Provenance (5 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_69f76e2259608190bf6788a132e0d139 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7aa71a66c81909dba6a2c3466284c |
completed | May 3, 2026, 8:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a390d2f1e1481909f622447321c46c9 |
completed | June 22, 2026, 10:23 a.m. |
| NEDg | Description generation | batch_6a390e01a0208190b82413f513663239 |
completed | June 22, 2026, 10:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a39102d69ac81908d9aefcb7c514717 |
completed | June 22, 2026, 10:36 a.m. |
Created at: May 3, 2026, 4:07 p.m.