Triple

T30230655
Position Surface form Disambiguated ID Type / Status
Subject Philadelphia Stars E768616 entity
Predicate notablePlayer P304 FINISHED
Object Chuck Fusina
Chuck Fusina is a former American football quarterback best known for leading the USFL’s Philadelphia Stars to multiple championship appearances after a standout college career at Penn State.
E1983194 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: Chuck Fusina | Statement: [Philadelphia Stars, notablePlayer, Chuck Fusina]
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: Chuck Fusina
Triple: [Philadelphia Stars, notablePlayer, Chuck Fusina]
Generated description
Chuck Fusina is a former American football quarterback best known for leading the USFL’s Philadelphia Stars to multiple championship appearances after a standout college career at Penn State.

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_69f2248108208190be60bf1af343ce70 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f68025551081908f282e9ae3efebe7 completed May 2, 2026, 10:52 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2e7fb509988190ac2634c3a00bc251 completed June 14, 2026, 10:17 a.m.
NEDg Description generation batch_6a2e8071a2c0819091ed05c5b73ce899 completed June 14, 2026, 10:20 a.m.
NED2 Entity disambiguation (via description) batch_6a2e84c760408190a96c2b029ab20381 completed June 14, 2026, 10:39 a.m.
Created at: April 29, 2026, 7:36 p.m.