Triple
T30346907
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jump Square |
E771890
|
entity |
| Predicate | serializedWork |
P161651
|
FINISHED |
| Object |
New Prince of Tennis
New Prince of Tennis is a sequel manga to The Prince of Tennis that continues the story of Ryoma Echizen and other tennis prodigies as they train and compete at an even higher level.
|
E1913573
|
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: New Prince of Tennis | Statement: [Jump Square, serializedWork, New Prince of Tennis]
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: New Prince of Tennis Triple: [Jump Square, serializedWork, New Prince of Tennis]
Generated description
New Prince of Tennis is a sequel manga to The Prince of Tennis that continues the story of Ryoma Echizen and other tennis prodigies as they train and compete at an even higher level.
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_69f2248b9a208190bc3e6804acd5afd6 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f682084db081909e261c5bbae024e3 |
completed | May 2, 2026, 11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a2789354ab48190ab09ffae5e0ca992 |
completed | June 9, 2026, 3:32 a.m. |
| NEDg | Description generation | batch_6a2789c460c08190a5fd22479b269e6b |
completed | June 9, 2026, 3:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a278a76f450819095acd3e2b23d2b73 |
completed | June 9, 2026, 3:37 a.m. |
Created at: April 29, 2026, 7:56 p.m.