Triple
T25945283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ladies in Black (2018 film) |
E653823
|
entity |
| Predicate | productionCompany |
P490
|
FINISHED |
| Object |
Story Bridge Films
Story Bridge Films is an Australian film production company known for developing and producing feature films such as the 2018 period comedy-drama "Ladies in Black."
|
E1701764
|
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: Story Bridge Films | Statement: [Ladies in Black (2018 film), productionCompany, Story Bridge Films]
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: Story Bridge Films Triple: [Ladies in Black (2018 film), productionCompany, Story Bridge Films]
Generated description
Story Bridge Films is an Australian film production company known for developing and producing feature films such as the 2018 period comedy-drama "Ladies in Black."
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_69e7ab40ac788190a771bc499eb1ae5f |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f60464d4988190bc39b78c8e418547 |
completed | May 2, 2026, 2:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a10ece4d2008190b90b1354c8f87edf |
completed | May 22, 2026, 11:55 p.m. |
| NEDg | Description generation | batch_6a10f0cbb3d48190ac16d04f9510ac6d |
completed | May 23, 2026, 12:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a10f15441048190a5e1285220fcea3d |
completed | May 23, 2026, 12:14 a.m. |
Created at: April 22, 2026, 8:42 a.m.