Triple
T27209266
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tintin in the Congo |
E683953
|
entity |
| Predicate | hasLibraryClassificationIssues |
P180700
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Tintin in the Congo, hasLibraryClassificationIssues, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLibraryClassificationIssues Context triple: [Tintin in the Congo, hasLibraryClassificationIssues, yes]
-
A.
usesLibraryClassificationSystem
Indicates that an entity organizes or categorizes its materials according to a formal library classification system.
-
B.
hasLCClassification
Indicates that an entity is assigned a specific Library of Congress Classification code representing its subject or shelving category.
-
C.
hasDeweyDecimalClassification
Indicates that an item (such as a book or resource) is assigned a specific Dewey Decimal Classification number representing its subject area in a library system.
-
D.
usesClassificationCriteria
Indicates that one entity applies specific classification criteria to categorize, organize, or evaluate another entity.
-
E.
hasIssueWith
Indicates that one entity experiences a problem, conflict, or concern related to another 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_69eefad339a08190aeacb2a198f1a39b |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69f74c70fd248190a9d5543afcb08211 |
completed | May 3, 2026, 1:24 p.m. |
| PD | Predicate disambiguation | batch_69f7478e3b548190a51d5d436e2bb036 |
completed | May 3, 2026, 1:03 p.m. |
| PDg | Predicate description generation | batch_69f74c6fa6548190b03935f65429a24e |
completed | May 3, 2026, 1:23 p.m. |
Created at: April 27, 2026, 9:39 a.m.