Triple
T10210007
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mildred Montag |
E242300
|
entity |
| Predicate | relationshipToBooks |
P92723
|
FINISHED |
| Object | indifferent to books |
—
|
LITERAL 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: indifferent to books | Statement: [Mildred Montag, relationshipToBooks, indifferent to books]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationshipToBooks Context triple: [Mildred Montag, relationshipToBooks, indifferent to books]
-
A.
hasAuthorRelationship
Indicates a relationship where one entity serves as the author or creator of another entity (such as a work, document, or resource).
-
B.
usesBookAs
Indicates that one entity employs or treats a book as a particular tool, resource, or role in a given context.
-
C.
bookMention
Indicates that one entity (such as a text, person, or source) makes reference to or cites a particular book.
-
D.
bookOwnershipModel
Indicates a relationship where the model captures or represents how ownership of books is assigned, structured, or managed between entities.
-
E.
numberOfBooks
Indicates the quantity of books associated with a given entity.
- F. None of above. chosen
Provenance (4 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_69d381ae26c48190985abd0e25ee5d04 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d3aa22071c819095febd18dd607978 |
completed | April 6, 2026, 12:42 p.m. |
| PD | Predicate disambiguation | batch_69d39559e5ac8190b88eca75956b7e6a |
completed | April 6, 2026, 11:13 a.m. |
| PDg | Predicate description generation | batch_69d3aa208c248190a0fb186b106389f3 |
completed | April 6, 2026, 12:42 p.m. |
Created at: April 6, 2026, 11:01 a.m.