Triple
T30164926
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eshnunna |
E766768
|
entity |
| Predicate | legalTextType |
P27701
|
FINISHED |
| Object | law code |
—
|
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: law code | Statement: [Eshnunna, legalTextType, law code]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalTextType Context triple: [Eshnunna, legalTextType, law code]
-
A.
legalContent
Indicates that the associated material complies with applicable laws and regulations and is permitted for use, distribution, or display.
-
B.
legalCodeType
Indicates the specific category or classification of a legal code that applies to an entity or situation.
-
C.
legalTextTypeWorkedOn
chosen
Indicates that an entity has worked on or handled a specific type or category of legal text.
-
D.
legalElement
Indicates that something is a constituent part or component required or recognized within a legal framework, rule, or process.
-
E.
legalForm
Indicates the specific legal structure or organizational type under which an entity is formally constituted and recognized by law.
- 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_69f2247a968881909d79c18f2bfcb275 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fde9fc184c8190bebef35df0e76076 |
completed | May 8, 2026, 1:49 p.m. |
| PD | Predicate disambiguation | batch_69fde6e5beb4819094945a695e961d88 |
completed | May 8, 2026, 1:36 p.m. |
Created at: April 29, 2026, 7:23 p.m.