Triple
T37499633
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Balls to Picasso |
E931921
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
1000 Points of Light
1000 Points of Light is a song by Bruce Dickinson featured on his 1994 solo album "Balls to Picasso."
|
E2227883
|
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: 1000 Points of Light | Statement: [Balls to Picasso, hasPart, 1000 Points of Light]
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: 1000 Points of Light Triple: [Balls to Picasso, hasPart, 1000 Points of Light]
Generated description
1000 Points of Light is a song by Bruce Dickinson featured on his 1994 solo album "Balls to Picasso."
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_69f76ec5268481909ea01c73aeeefd42 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fba383d4d48190b9a06a193d10df28 |
completed | May 6, 2026, 8:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a408c4f15b08190b4892d9524d7b206 |
completed | June 28, 2026, 2:51 a.m. |
| NEDg | Description generation | batch_6a408cc01d0881908751137e434f2214 |
completed | June 28, 2026, 2:53 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a408d1ff7f0819098e9ad8cf001b19e |
completed | June 28, 2026, 2:55 a.m. |
Created at: May 3, 2026, 4:17 p.m.