Triple
T23961347
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Rain in Spain |
E603937
|
entity |
| Predicate | featuredIn |
P626
|
FINISHED |
| Object |
stage musical My Fair Lady
My Fair Lady is a classic stage musical, adapted from George Bernard Shaw’s play Pygmalion, that follows phonetics professor Henry Higgins as he transforms Cockney flower girl Eliza Doolittle into a refined lady of high society.
|
E55158
|
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: stage musical My Fair Lady | Statement: [The Rain in Spain, featuredIn, stage musical My Fair Lady]
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: stage musical My Fair Lady Triple: [The Rain in Spain, featuredIn, stage musical My Fair Lady]
Generated description
My Fair Lady is a classic stage musical, adapted from George Bernard Shaw’s play Pygmalion, that follows phonetics professor Henry Higgins as he transforms Cockney flower girl Eliza Doolittle into a refined lady of high society.
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_69e2954222288190a7323554d0cca8d7 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f1d0dac8e081908286e8d8d30784ee |
completed | April 29, 2026, 9:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0f765af0988190a1d62c6af6149a0b |
completed | May 21, 2026, 9:17 p.m. |
| NEDg | Description generation | batch_6a0f770a063c81909f356346c9c521ad |
completed | May 21, 2026, 9:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0f782b47c08190a221c196ddc9d566 |
completed | May 21, 2026, 9:24 p.m. |
Created at: April 17, 2026, 9:23 p.m.