Triple
T27216574
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amalgamated Press |
E681154
|
entity |
| Predicate | product |
P490
|
FINISHED |
| Object |
The People’s Friend
The People’s Friend is a long-running British weekly women’s magazine known for its wholesome fiction, craft patterns, and family-oriented features.
|
E1759289
|
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 People’s Friend | Statement: [Amalgamated Press, product, The People’s Friend]
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 People’s Friend Triple: [Amalgamated Press, product, The People’s Friend]
Generated description
The People’s Friend is a long-running British weekly women’s magazine known for its wholesome fiction, craft patterns, and family-oriented features.
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_69eefac9f64c8190a07490fe0c8b72a3 |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69f6261dc32c8190b1a016a85c204af1 |
completed | May 2, 2026, 4:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a1253aaf5b88190a053e3f8cb9a8881 |
completed | May 24, 2026, 1:26 a.m. |
| NEDg | Description generation | batch_6a1254349d388190b85474fb0a86bab3 |
completed | May 24, 2026, 1:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a1254fc697c8190baf4f8adefcea4d2 |
completed | May 24, 2026, 1:31 a.m. |
Created at: April 27, 2026, 9:41 a.m.