Triple
T7125191
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Code of Civil Procedure, 1908 |
E166041
|
entity |
| Predicate | numberOfSectionsApprox |
P1632
|
FINISHED |
| Object | 158 |
—
|
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: 158 | Statement: [Code of Civil Procedure, 1908, numberOfSectionsApprox, 158]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfSectionsApprox Context triple: [Code of Civil Procedure, 1908, numberOfSectionsApprox, 158]
-
A.
hasSectionCount
chosen
Indicates that an entity is associated with a specific number of sections it contains or comprises.
-
B.
numberOfFragmentsApprox
Indicates an approximate count of how many fragments or pieces are associated with the subject.
-
C.
numberOfCells
Indicates the total count of individual cells associated with or contained in a given entity.
-
D.
hasPageCountApprox
Indicates that an entity is associated with an approximate or estimated number of pages, rather than an exact page count.
-
E.
hasSectionLength
Indicates that an entity is associated with a specific length value for one of its sections.
- 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_69c6888350588190870cd552b427a1cd |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e64c0f688190a9b7482d86c2f033 |
completed | March 27, 2026, 8:19 p.m. |
| PD | Predicate disambiguation | batch_69c6e1c7289881909f3b533c384f9ed4 |
completed | March 27, 2026, 8 p.m. |
Created at: March 27, 2026, 2:44 p.m.