Triple

T24923831
Position Surface form Disambiguated ID Type / Status
Subject J. Hillis Miller E618794 entity
Predicate notableWork P4 FINISHED
Object The Linguistic Moment
The Linguistic Moment is a seminal work of literary theory by J. Hillis Miller that explores how meaning and interpretation emerge from the intricate play of language in literary texts.
 E1657639 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 Linguistic Moment | Statement: [J. Hillis Miller, notableWork, The Linguistic Moment]
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 Linguistic Moment
Triple: [J. Hillis Miller, notableWork, The Linguistic Moment]
Generated description
The Linguistic Moment is a seminal work of literary theory by J. Hillis Miller that explores how meaning and interpretation emerge from the intricate play of language in literary texts.

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_69e2fab9edd88190b86004a78a28bc20 completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f423adaae48190885fe3dab1961b34 completed May 1, 2026, 3:53 a.m.
NED1 Entity disambiguation (via context triple) batch_6a10333489c881909e0aab26a9b06b22 completed May 22, 2026, 10:43 a.m.
NEDg Description generation batch_6a1033eeacac81909e208f3b3e17190e completed May 22, 2026, 10:46 a.m.
NED2 Entity disambiguation (via description) batch_6a1034d8d52481908c5c422f943c683b completed May 22, 2026, 10:50 a.m.
Created at: April 18, 2026, 5:29 a.m.