Triple
T35565504
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Atahualpa Yupanqui |
E1027760
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Luna tucumana
"Luna tucumana" is a renowned Argentine folk song, widely considered one of Atahualpa Yupanqui’s most emblematic and enduring compositions.
|
E2146767
|
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: Luna tucumana | Statement: [Atahualpa Yupanqui, notableWork, Luna tucumana]
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: Luna tucumana Triple: [Atahualpa Yupanqui, notableWork, Luna tucumana]
Generated description
"Luna tucumana" is a renowned Argentine folk song, widely considered one of Atahualpa Yupanqui’s most emblematic and enduring compositions.
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_69f76e020fd8819081cb080e7e203083 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7987d08a08190b530a67af5735b1a |
completed | May 3, 2026, 6:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3852fd4b7081908abb235e183c2296 |
completed | June 21, 2026, 9:09 p.m. |
| NEDg | Description generation | batch_6a385390b15c81908b6117605f1ec6cd |
completed | June 21, 2026, 9:11 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a38547dd57c819093fa90fb12160ad9 |
completed | June 21, 2026, 9:15 p.m. |
Created at: May 3, 2026, 4:04 p.m.