Triple
T27402255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lucile Gleason |
E691882
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
The Clock Strikes Twelve
The Clock Strikes Twelve is a film associated with American actress Lucile Gleason, remembered as one of her notable screen roles.
|
E1769575
|
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: The Clock Strikes Twelve | Statement: [Lucile Gleason, notableWork, The Clock Strikes Twelve]
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: The Clock Strikes Twelve Triple: [Lucile Gleason, notableWork, The Clock Strikes Twelve]
Generated description
The Clock Strikes Twelve is a film associated with American actress Lucile Gleason, remembered as one of her notable screen roles.
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_69ef5204f7048190bf226a129858fc5b |
completed | April 27, 2026, 12:09 p.m. |
| NER | Named-entity recognition | batch_69f62cd3a0e8819095fc30c4f4ac6def |
completed | May 2, 2026, 4:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a12a7faa75081908cdf347c592479fc |
completed | May 24, 2026, 7:25 a.m. |
| NEDg | Description generation | batch_6a12a949b620819092007b2ee7e96064 |
completed | May 24, 2026, 7:31 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a12aa9670988190be61c9aaa57b70c9 |
completed | May 24, 2026, 7:36 a.m. |
Created at: April 27, 2026, 12:29 p.m.