Triple
T9661545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Code of Lipit-Ishtar |
E233599
|
entity |
| Predicate | approximateNumberOfLaws |
P12093
|
FINISHED |
| Object | about 50 laws (partially preserved) |
—
|
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 50 laws (partially preserved) | Statement: [Code of Lipit-Ishtar, approximateNumberOfLaws, about 50 laws (partially preserved)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateNumberOfLaws Context triple: [Code of Lipit-Ishtar, approximateNumberOfLaws, about 50 laws (partially preserved)]
-
A.
numberOfLaws
chosen
Indicates the quantitative count of laws associated with a given entity or context.
-
B.
numberOfLawyers
Indicates the quantity of lawyers associated with a given entity or situation.
-
C.
publicLawNumber
Indicates the specific public law identifier associated with a legislative act or statute.
-
D.
hasStatuteLaw
Indicates that a jurisdiction or entity is governed by, or possesses, a body of formal written laws enacted by a legislative authority.
-
E.
introducedInLaws
Indicates that one entity caused another entity to meet or become acquainted with their in-laws.
- 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_69ca848d3b6c8190ae98ea554dea58df |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9c0a15e4819092ea0fb2cb6e1c12 |
completed | April 1, 2026, 10:28 p.m. |
| PD | Predicate disambiguation | batch_69ccd5b3239c8190b3ae3b9bd121e4bd |
completed | April 1, 2026, 8:22 a.m. |
Created at: March 30, 2026, 8:14 p.m.