Triple
T7386117
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Economy of Jersey |
E170383
|
entity |
| Predicate | laborForceSector |
P69121
|
FINISHED |
| Object | predominantly services |
—
|
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: predominantly services | Statement: [Economy of Jersey, laborForceSector, predominantly services]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: laborForceSector Context triple: [Economy of Jersey, laborForceSector, predominantly services]
-
A.
ownerSector
Indicates the sector or industry category to which the owner of an entity belongs.
-
B.
hasOccupationSector
Indicates that an entity’s occupation belongs to or is categorized within a particular economic or professional sector.
-
C.
occupationType
Indicates the specific kind or category of work, profession, or role that an entity performs or holds.
-
D.
laborForceCharacteristic
Indicates a relationship where an entity is described or classified by a specific attribute or status related to its participation in the labor force.
-
E.
hasTertiaryEconomicSector
chosen
Indicates that an entity participates in or possesses activities belonging to the tertiary (service) sector of the economy, such as services rather than primary or secondary production.
- 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_69c68a5e2c9081909e713ce866e0060a |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f1f117ec8190a97cbd0b35d5811a |
completed | March 27, 2026, 9:09 p.m. |
| PD | Predicate disambiguation | batch_69c6f0309cc88190b55d278969400294 |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:08 p.m.