Triple
T36505154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siege of Harfleur |
E899440
|
entity |
| Predicate | hasEffectOnCampaign |
P11098
|
FINISHED |
| Object | reduced English army strength before Agincourt |
—
|
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: reduced English army strength before Agincourt | Statement: [Siege of Harfleur, hasEffectOnCampaign, reduced English army strength before Agincourt]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEffectOnCampaign Context triple: [Siege of Harfleur, hasEffectOnCampaign, reduced English army strength before Agincourt]
-
A.
effectOnCampaign
chosen
Indicates the influence or impact that one factor has on the outcome or performance of a campaign.
-
B.
canImpact
Indicates that one entity has the potential or ability to affect, influence, or cause a change in another entity.
-
C.
hasEffectIn
Indicates that one entity produces, causes, or exerts an effect within a specified context, system, or environment.
-
D.
hasDirectEffect
Indicates that one entity produces an immediate and unmediated impact or change on another entity.
-
E.
hasMarketingCampaign
Indicates that an entity is associated with or utilizes a specific marketing campaign.
- 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_69f76e5b92088190933afda3f7531dd4 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7c371931c8190afb1d4dd5157f92c |
completed | May 3, 2026, 9:51 p.m. |
| PD | Predicate disambiguation | batch_69f7c1b91fd88190ab85afd626603769 |
completed | May 3, 2026, 9:44 p.m. |
Created at: May 3, 2026, 4:10 p.m.