Triple

T31147656
Position Surface form Disambiguated ID Type / Status
Subject Barbara Britton E793972 entity
Predicate notableWork P4 FINISHED
Object The Bandit of Sherwood Forest
The Bandit of Sherwood Forest is a 1946 swashbuckling adventure film that continues the Robin Hood legend by focusing on the exploits of his son.
 E1950406 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: The Bandit of Sherwood Forest | Statement: [Barbara Britton, notableWork, The Bandit of Sherwood Forest]
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: The Bandit of Sherwood Forest
Triple: [Barbara Britton, notableWork, The Bandit of Sherwood Forest]
Generated description
The Bandit of Sherwood Forest is a 1946 swashbuckling adventure film that continues the Robin Hood legend by focusing on the exploits of his son.

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_69f224d41bb48190a5621cd1485e3a30 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f697ebd0d081909ff87ca7cd1c5459 completed May 3, 2026, 12:33 a.m.
NED1 Entity disambiguation (via context triple) batch_6a294720cac081908998e6c99c0258ae completed June 10, 2026, 11:14 a.m.
NEDg Description generation batch_6a294edd87888190a40f71d4d7f57b18 completed June 10, 2026, 11:47 a.m.
NED2 Entity disambiguation (via description) batch_6a2950ac30e88190a3f55d5a68f317d8 completed June 10, 2026, 11:55 a.m.
Created at: April 29, 2026, 9:06 p.m.