Triple
T34837760
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Icon Award |
E1004245
|
entity |
| Predicate | hasAlternativeLabel |
P39
|
FINISHED |
| Object |
ESPY Icon Award
The ESPY Icon Award is a special honor presented at the ESPY Awards to recognize individuals whose achievements and impact have left a lasting legacy in the world of sports.
|
E2114462
|
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: ESPY Icon Award | Statement: [Icon Award, hasAlternativeLabel, ESPY Icon Award]
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: ESPY Icon Award Triple: [Icon Award, hasAlternativeLabel, ESPY Icon Award]
Generated description
The ESPY Icon Award is a special honor presented at the ESPY Awards to recognize individuals whose achievements and impact have left a lasting legacy in the world of sports.
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_69f76db97714819099b5bed36fd64e9d |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f7810f5bdc8190b3f7c714f17eefce |
completed | May 3, 2026, 5:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a37794c6684819090dfd1e890662c0a |
completed | June 21, 2026, 5:40 a.m. |
| NEDg | Description generation | batch_6a3779d574e481909c73b8be299eae8f |
completed | June 21, 2026, 5:42 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a377a7836bc8190a77adab5c1c04df7 |
completed | June 21, 2026, 5:45 a.m. |
Created at: May 3, 2026, 4 p.m.