Triple

T29270667
Position Surface form Disambiguated ID Type / Status
Subject Petitgout E742104 entity
Predicate notableBearer P458 FINISHED
Object Luke Petitgout
Luke Petitgout is a former American football offensive tackle who played primarily for the New York Giants in the NFL.
E1872820 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: Luke Petitgout | Statement: [Petitgout, notableBearer, Luke Petitgout]
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: Luke Petitgout
Triple: [Petitgout, notableBearer, Luke Petitgout]
Generated description
Luke Petitgout is a former American football offensive tackle who played primarily for the New York Giants in the NFL.

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_69f0912124d48190a046642b69407f4c completed April 28, 2026, 10:51 a.m.
NER Named-entity recognition batch_69f664e35fb88190a0f8c0fee74f32fb completed May 2, 2026, 8:56 p.m.
NED1 Entity disambiguation (via context triple) batch_6a260c011a90819082039f0f5a7227d5 completed June 8, 2026, 12:25 a.m.
NEDg Description generation batch_6a26103b50948190a67b288cf9f474ce completed June 8, 2026, 12:43 a.m.
NED2 Entity disambiguation (via description) batch_6a261bab40048190b31f5b12454bedbf completed June 8, 2026, 1:32 a.m.
Created at: April 28, 2026, 12:48 p.m.