Triple
T23999551
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maria Schell |
E594204
|
entity |
| Predicate | birthName |
P65
|
FINISHED |
| Object |
Maria Margarethe Anna Schell
Maria Margarethe Anna Schell was an acclaimed Austrian-Swiss actress known for her emotionally intense performances in European cinema from the 1940s through the 1960s.
|
E1643106
|
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: Maria Margarethe Anna Schell | Statement: [Maria Schell, birthName, Maria Margarethe Anna Schell]
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: Maria Margarethe Anna Schell Triple: [Maria Schell, birthName, Maria Margarethe Anna Schell]
Generated description
Maria Margarethe Anna Schell was an acclaimed Austrian-Swiss actress known for her emotionally intense performances in European cinema from the 1940s through the 1960s.
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_69e288b9ecf08190b8c94a278f5674fe |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1d46289b881909ea351cdc3f57c43 |
completed | April 29, 2026, 9:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0ff82c7a188190981277f20a385a16 |
completed | May 22, 2026, 6:31 a.m. |
| NEDg | Description generation | batch_6a0ff9076a7081908cd02686d3ac6080 |
completed | May 22, 2026, 6:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0ffcc3dc0c8190a4b8e0b3d68e8c0a |
completed | May 22, 2026, 6:50 a.m. |
Created at: April 17, 2026, 9:39 p.m.