Triple
T7386124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Economy of Jersey |
E170383
|
entity |
| Predicate | sectorShare |
P71
|
FINISHED |
| Object | financial services contribute a large share of GVA |
—
|
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: financial services contribute a large share of GVA | Statement: [Economy of Jersey, sectorShare, financial services contribute a large share of GVA]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sectorShare Context triple: [Economy of Jersey, sectorShare, financial services contribute a large share of GVA]
-
A.
sharesMarket
Indicates that two entities operate within or target the same market or customer segment.
-
B.
sectorNeutral
Indicates that the relationship or action is independent of, or unaffected by, the specific sector or industry of the entities involved.
-
C.
sector
chosen
Indicates that an entity operates in, belongs to, or is associated with a particular economic or industrial sector.
-
D.
sectorInfluence
Indicates the degree to which one sector affects, shapes, or exerts control over another sector or over outcomes within that sector.
-
E.
sharesSymbol
Indicates that two entities use or are associated with the same identifying symbol (such as a ticker, code, or notation).
- 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.