Triple
T38490422
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montgomery City Lines |
E918038
|
entity |
| Predicate | economicImpactOfBoycott |
P45127
|
FINISHED |
| Object | severe loss of revenue |
—
|
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: severe loss of revenue | Statement: [Montgomery City Lines, economicImpactOfBoycott, severe loss of revenue]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: economicImpactOfBoycott Context triple: [Montgomery City Lines, economicImpactOfBoycott, severe loss of revenue]
-
A.
impactOnSanctions
Indicates the effect or influence that one action, event, or condition has on the imposition, severity, or modification of sanctions.
-
B.
impactOnEconomy
Indicates the effect or influence that one factor, event, or action has on the state or performance of an economy.
-
C.
stanceOnEconomy
Indicates a subject's expressed position, opinion, or policy view regarding economic issues or economic policy.
-
D.
impactOnTrade
Indicates a relationship where one entity causes or contributes to a change in the trade activities, volume, or conditions affecting another entity.
-
E.
impactOnBusiness
chosen
Indicates the effect or influence that one factor, event, or action has on a business’s performance, operations, or outcomes.
- 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_69f76e9894208190a129a553a60ca58c |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fcd313e61c8190b174b331365b803f |
completed | May 7, 2026, 5:59 p.m. |
| PD | Predicate disambiguation | batch_69fcd1f6b2e08190bf0300ae7c9ae67a |
completed | May 7, 2026, 5:55 p.m. |
Created at: May 3, 2026, 4:31 p.m.