Triple
T25357579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Medicine Man (1930 film) |
E635865
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object |
Eva Novak
Eva Novak was an American silent film actress who appeared in numerous Westerns and comedies during the 1910s and 1920s.
|
E1675425
|
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: Eva Novak | Statement: [The Medicine Man (1930 film), stars, Eva Novak]
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: Eva Novak Triple: [The Medicine Man (1930 film), stars, Eva Novak]
Generated description
Eva Novak was an American silent film actress who appeared in numerous Westerns and comedies during the 1910s and 1920s.
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_69e75a9b7cf481909f2dcdfb37d95ca7 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f49e0263e081909449045f434eac6c |
completed | May 1, 2026, 12:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a1075ff65588190a26ead435750dafa |
completed | May 22, 2026, 3:27 p.m. |
| NEDg | Description generation | batch_6a1076991b208190945d037fd9eef5f2 |
completed | May 22, 2026, 3:30 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a1077b79abc819099f92e2e2cc19c5d |
completed | May 22, 2026, 3:35 p.m. |
Created at: April 21, 2026, 1:36 p.m.