Triple
T19142689
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spanish economic miracle |
E468596
|
entity |
| Predicate | sectorMostAffected |
P134585
|
FINISHED |
| Object | industry |
—
|
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: industry | Statement: [Spanish economic miracle, sectorMostAffected, industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sectorMostAffected Context triple: [Spanish economic miracle, sectorMostAffected, industry]
-
A.
sectorBenefited
Indicates that a particular sector gains advantage, support, or positive impact from a given action, policy, resource, or entity.
-
B.
dominatedSector
Indicates that one entity exercises prevailing control or influence over a particular sector relative to others.
-
C.
sectorStrength
Indicates the relative performance or influence level of a specific sector compared to others within a broader system or market.
-
D.
sectorInfluence
Indicates the degree to which one sector affects, shapes, or exerts control over another sector or over outcomes within that sector.
-
E.
affectedCity
Indicates that a particular city is impacted or influenced by a specified event, action, or condition.
- 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_69d8dd0796a48190b34ce4cd9d3f3be5 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e976e340819098efccc30b2eef0e |
completed | April 20, 2026, 8:53 a.m. |
| PD | Predicate disambiguation | batch_69e4b9b475d88190a8c15e8eb01dbfef |
completed | April 19, 2026, 11:17 a.m. |
| PDg | Predicate description generation | batch_69e4bfe9ef7081908a74a57d1fc731ea |
completed | April 19, 2026, 11:43 a.m. |
Created at: April 10, 2026, 12:05 p.m.