Triple
T35638368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jersey law |
E1029782
|
entity |
| Predicate | hasDistinctArea |
P199630
|
FINISHED |
| Object | Jersey contract law |
—
|
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: Jersey contract law | Statement: [Jersey law, hasDistinctArea, Jersey contract law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDistinctArea Context triple: [Jersey law, hasDistinctArea, Jersey contract law]
-
A.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
B.
hasAreaNumber
Indicates that an entity is associated with a specific area identified by a numerical code.
-
C.
hasAreaRange
Indicates that something’s area falls within a specified minimum-to-maximum range.
-
D.
hasCollectionArea
Indicates that an entity is associated with a specific geographic or spatial area from which items, specimens, or data are collected.
-
E.
hasAreaTotal
Indicates the total surface area associated with an entity, typically measured over its entire extent.
- F. None of above. chosen
Provenance (4 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_69f76e087bdc8190a4794bf9c0bd7634 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69ff49016dcc8190a8a43868c728b4f1 |
completed | May 9, 2026, 2:47 p.m. |
| PD | Predicate disambiguation | batch_69ff4891924c8190b5be340e2520e012 |
completed | May 9, 2026, 2:45 p.m. |
| PDg | Predicate description generation | batch_69ff490096388190832a90e9abbc8d81 |
completed | May 9, 2026, 2:47 p.m. |
Created at: May 3, 2026, 4:05 p.m.