Triple
T1334198
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | German Civil Code |
E28709
|
entity |
| Predicate | hasSectionCountApprox |
P1632
|
FINISHED |
| Object | over 2300 sections |
—
|
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: over 2300 sections | Statement: [German Civil Code, hasSectionCountApprox, over 2300 sections]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSectionCountApprox Context triple: [German Civil Code, hasSectionCountApprox, over 2300 sections]
-
A.
hasSectionCount
chosen
Indicates that an entity is associated with a specific number of sections it contains or comprises.
-
B.
hasPageCountApprox
Indicates that an entity is associated with an approximate or estimated number of pages, rather than an exact page count.
-
C.
hasSect
Indicates that an entity includes, contains, or is associated with a particular sect or subgroup within a larger religious, ideological, or organizational context.
-
D.
hasNumberOfDivisions
Indicates the relationship that specifies how many divisions or subunits an entity possesses.
-
E.
hasApproximateNumberOfLetters
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
- F. None of above.
Provenance (3 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_69a498561a508190a3e1bc137c2b866a |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c1e98900819092c54c0fb58b958a |
completed | March 1, 2026, 10:47 p.m. |
| PD | Predicate disambiguation | batch_69a4bef174708190a07bbc697fe19a2d |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:55 p.m.