Triple

T31399993
Position Surface form Disambiguated ID Type / Status
Subject Sir Louis Cavagnari E800970 entity
Predicate positionHeld P8 FINISHED
Object British Resident in Kabul
The British Resident in Kabul was the chief diplomatic and political representative of the British Empire at the Afghan court during the late 19th century.
 E1961232 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: British Resident in Kabul | Statement: [Sir Louis Cavagnari, positionHeld, British Resident in Kabul]
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: British Resident in Kabul
Triple: [Sir Louis Cavagnari, positionHeld, British Resident in Kabul]
Generated description
The British Resident in Kabul was the chief diplomatic and political representative of the British Empire at the Afghan court during the late 19th century.

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_69f224ea9998819086ae2e4f4f4091c8 completed April 29, 2026, 3:34 p.m.
NER Named-entity recognition batch_69f6a05c04ec819096d2e794de024144 completed May 3, 2026, 1:09 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2ad24c7e908190a14258d96c9de802 completed June 11, 2026, 3:20 p.m.
NEDg Description generation batch_6a2ae9b7855c8190aa8f2b2c0b876fe8 completed June 11, 2026, 5 p.m.
NED2 Entity disambiguation (via description) batch_6a2aea1554248190b1801663c0a4b67c completed June 11, 2026, 5:02 p.m.
Created at: April 29, 2026, 9:19 p.m.