Triple

T33903411
Position Surface form Disambiguated ID Type / Status
Subject Dr. Haggett E869112 entity
Predicate hasFamily P3600 FINISHED
Object Susan Haggett
Susan Haggett is a family member of Dr. Haggett, about whom no further public biographical information is readily available.
E2106347 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: Susan Haggett | Statement: [Dr. Haggett, hasFamily, Susan Haggett]
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: Susan Haggett
Triple: [Dr. Haggett, hasFamily, Susan Haggett]
Generated description
Susan Haggett is a family member of Dr. Haggett, about whom no further public biographical information is readily available.

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_69f34997703c8190866b1d404bce531f completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f70184a4d081908a11fdf7a221c302 completed May 3, 2026, 8:04 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3748cf9b2c8190b09b0208c0f8bf39 completed June 21, 2026, 2:13 a.m.
NEDg Description generation batch_6a3749ba56f48190a61b653a4a0af817 completed June 21, 2026, 2:17 a.m.
NED2 Entity disambiguation (via description) batch_6a374a3c4f248190873958f2f5f5f62e completed June 21, 2026, 2:19 a.m.
Created at: May 1, 2026, 1:48 a.m.