Triple
T29846777
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hotel Imperial (1927 film) |
E757949
|
entity |
| Predicate | starredActor |
P5563
|
FINISHED |
| Object |
Nicholas Soussanin
Nicholas Soussanin was a Russian-born actor active in early 20th-century cinema, known for his character roles in silent and early sound films.
|
E1886471
|
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: Nicholas Soussanin | Statement: [Hotel Imperial (1927 film), starredActor, Nicholas Soussanin]
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: Nicholas Soussanin Triple: [Hotel Imperial (1927 film), starredActor, Nicholas Soussanin]
Generated description
Nicholas Soussanin was a Russian-born actor active in early 20th-century cinema, known for his character roles in silent and early sound films.
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_69f2245a82cc8190a387e7d0118d710b |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f676438624819086aabd1b6e5fef4c |
completed | May 2, 2026, 10:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a26e60fc6bc81909e071621f47bf035 |
completed | June 8, 2026, 3:56 p.m. |
| NEDg | Description generation | batch_6a26e6e147d081909cd31eda74a95a11 |
completed | June 8, 2026, 3:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a26eabd98e081908d444da8db7f2183 |
completed | June 8, 2026, 4:15 p.m. |
Created at: April 29, 2026, 5:42 p.m.