Triple
T24305501
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charlie Chaplin stock company |
E612523
|
entity |
| Predicate | notableMember |
P10
|
FINISHED |
| Object |
Loyal Underwood
Loyal Underwood was an American silent film actor best known for his frequent comic supporting roles alongside Charlie Chaplin in the 1910s.
|
E1627483
|
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: Loyal Underwood | Statement: [Charlie Chaplin stock company, notableMember, Loyal Underwood]
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: Loyal Underwood Triple: [Charlie Chaplin stock company, notableMember, Loyal Underwood]
Generated description
Loyal Underwood was an American silent film actor best known for his frequent comic supporting roles alongside Charlie Chaplin in the 1910s.
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_69e2d7d91bb48190bc5377d17a85fb21 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f2922641bc81908fa3595941e60741 |
completed | April 29, 2026, 11:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0fc9d703f08190b8ad09766240615a |
completed | May 22, 2026, 3:13 a.m. |
| NEDg | Description generation | batch_6a0fcb81601081908b19b049af450c9f |
completed | May 22, 2026, 3:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0fcc14b8748190bb8d137c57011cce |
completed | May 22, 2026, 3:23 a.m. |
Created at: April 18, 2026, 1:30 a.m.