Triple
T23926048
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Mummy’s Hand |
E602354
|
entity |
| Predicate | director |
P255
|
FINISHED |
| Object |
Christy Cabanne
Christy Cabanne was an American film director and screenwriter active from the silent era through the 1940s, known for his prolific work across genres in Hollywood.
|
E1609702
|
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: Christy Cabanne | Statement: [The Mummy’s Hand, director, Christy Cabanne]
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: Christy Cabanne Triple: [The Mummy’s Hand, director, Christy Cabanne]
Generated description
Christy Cabanne was an American film director and screenwriter active from the silent era through the 1940s, known for his prolific work across genres in Hollywood.
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_69e2953b928c819095395fa87baca583 |
completed | April 17, 2026, 8:16 p.m. |
| NER | Named-entity recognition | batch_69f1cf1ce7f88190a4afd091b4384558 |
completed | April 29, 2026, 9:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0f76415a2081909f9871abb75bac8a |
completed | May 21, 2026, 9:16 p.m. |
| NEDg | Description generation | batch_6a0f7773f5888190a2e56a10cb3be20a |
completed | May 21, 2026, 9:21 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0f78f8eb04819081723a908dfc2e9c |
completed | May 21, 2026, 9:28 p.m. |
Created at: April 17, 2026, 8:46 p.m.