Triple
T6243595
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UN General Assembly Resolution 194 |
E139660
|
entity |
| Predicate | hasNumberOfParagraphs |
P69278
|
FINISHED |
| Object | 15 |
—
|
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: 15 | Statement: [UN General Assembly Resolution 194, hasNumberOfParagraphs, 15]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfParagraphs Context triple: [UN General Assembly Resolution 194, hasNumberOfParagraphs, 15]
-
A.
hasParagraph
Indicates that one entity contains or is associated with a specific paragraph as part of its content or structure.
-
B.
hasTwoParagraphs
Indicates that the related content or text is composed of exactly two distinct paragraphs.
-
C.
hasNumberOfLines
Indicates the relationship that specifies how many lines are associated with a given entity.
-
D.
numberOfMainTexts
Indicates the quantity of primary or main textual components associated with an entity.
-
E.
numberOfPassages
Indicates the total count of distinct passages associated with or contained within a given entity or context.
- 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_69c008b1c5088190ae6de2555fc05ad8 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0631b32308190a8211043d1caa6e6 |
completed | March 22, 2026, 9:46 p.m. |
| PD | Predicate disambiguation | batch_69c056037bf88190a0a3fe7429345d0b |
completed | March 22, 2026, 8:50 p.m. |
| PDg | Predicate description generation | batch_69c056df95ac8190bc5efe050d3af864 |
completed | March 22, 2026, 8:53 p.m. |
Created at: March 22, 2026, 4:23 p.m.