Triple
T36518333
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Babolat |
E900107
|
entity |
| Predicate | notableRacquetLine |
P202946
|
FINISHED |
| Object |
Pure Strike
Pure Strike is a popular Babolat tennis racquet line designed for control-oriented, aggressive players seeking precision and stability from the baseline.
|
E2188040
|
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: Pure Strike | Statement: [Babolat, notableRacquetLine, Pure Strike]
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: Pure Strike Triple: [Babolat, notableRacquetLine, Pure Strike]
Generated description
Pure Strike is a popular Babolat tennis racquet line designed for control-oriented, aggressive players seeking precision and stability from the baseline.
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_69f76e5dada881909da2d34bc7a9202a |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a03809bd57c8190beb371feaf44a7db |
completed | May 12, 2026, 7:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a39dbdded5c819096dbf716e1afddc6 |
completed | June 23, 2026, 1:05 a.m. |
| NEDg | Description generation | batch_6a39dd5a787c819089c278f86de8078c |
completed | June 23, 2026, 1:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a39e17cb03c8190830fe006a9dd4455 |
completed | June 23, 2026, 1:29 a.m. |
Created at: May 3, 2026, 4:11 p.m.