Triple

T28848020
Position Surface form Disambiguated ID Type / Status
Subject Ropes & Gray E728507 entity
Predicate foundedBy P104 FINISHED
Object John Chipman Gray
John Chipman Gray was an American lawyer, legal scholar, and Harvard Law School professor best known for co-founding the prominent law firm Ropes & Gray and for his influential writings on property law.
 E1844144 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: John Chipman Gray | Statement: [Ropes & Gray, foundedBy, John Chipman Gray]
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: John Chipman Gray
Triple: [Ropes & Gray, foundedBy, John Chipman Gray]
Generated description
John Chipman Gray was an American lawyer, legal scholar, and Harvard Law School professor best known for co-founding the prominent law firm Ropes & Gray and for his influential writings on property law.

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_69f0319e8e7c8190b37288c8845b9dbc completed April 28, 2026, 4:03 a.m.
NER Named-entity recognition batch_69f659d651e08190bbd11dd3013f849c completed May 2, 2026, 8:08 p.m.
NED1 Entity disambiguation (via context triple) batch_6a250599aafc8190ae7291b82f3dbdad completed June 7, 2026, 5:46 a.m.
NEDg Description generation batch_6a25107f02208190a0ec977790cd59c0 completed June 7, 2026, 6:32 a.m.
NED2 Entity disambiguation (via description) batch_6a251129deac819099c205fe0d82b074 completed June 7, 2026, 6:35 a.m.
Created at: April 28, 2026, 6:43 a.m.