Triple
T2102007
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Code of Ur-Nammu |
E37110
|
entity |
| Predicate | originalNumberOfLawsEstimated |
P12093
|
FINISHED |
| Object | about 57 |
—
|
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: about 57 | Statement: [Code of Ur-Nammu, originalNumberOfLawsEstimated, about 57]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originalNumberOfLawsEstimated Context triple: [Code of Ur-Nammu, originalNumberOfLawsEstimated, about 57]
-
A.
numberOfLaws
chosen
Indicates the quantitative count of laws associated with a given entity or context.
-
B.
publicLawNumber
Indicates the specific public law identifier associated with a legislative act or statute.
-
C.
numberOfEdicts
Indicates the total count of edicts associated with or issued by a given entity.
-
D.
numberOfConstitutionsPreviouslyInForce
Indicates the count of distinct constitutions that were officially in force before the current one.
-
E.
containsLawOn
Indicates that one entity (such as a document, code, or regulation) includes or sets forth legal provisions concerning another entity or subject.
- 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_69a8861828948190924aa30c08806b3a |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abbabc83a8819091f786f21d33b5a6 |
completed | March 7, 2026, 5:42 a.m. |
| PD | Predicate disambiguation | batch_69abb7b7b6288190afa11b4d93bd5666 |
completed | March 7, 2026, 5:29 a.m. |
Created at: March 4, 2026, 7:43 p.m.