Triple
T27769486
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ACC Championship 2013 |
E701700
|
entity |
| Predicate | notablePlayer_FloridaState |
P201888
|
FINISHED |
| Object |
Rashad Greene
Rashad Greene is a former Florida State Seminoles wide receiver who became one of the program’s most productive pass-catchers before playing in the NFL for the Jacksonville Jaguars.
|
E1794405
|
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: Rashad Greene | Statement: [ACC Championship 2013, notablePlayer_FloridaState, Rashad Greene]
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: Rashad Greene Triple: [ACC Championship 2013, notablePlayer_FloridaState, Rashad Greene]
Generated description
Rashad Greene is a former Florida State Seminoles wide receiver who became one of the program’s most productive pass-catchers before playing in the NFL for the Jacksonville Jaguars.
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_69ef6a52fa708190934a32308d2c92dc |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_6a002f56113c8190a75c77827b159f5b |
completed | May 10, 2026, 7:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a13033ac88081908500f92ce1653432 |
completed | May 24, 2026, 1:55 p.m. |
| NEDg | Description generation | batch_6a13041668688190ae7b83c139db490d |
completed | May 24, 2026, 1:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a130608e7648190b7666813a297e308 |
completed | May 24, 2026, 2:07 p.m. |
Created at: April 27, 2026, 4:33 p.m.