Triple
T447699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Lagos |
E7056
|
entity |
| Predicate | FrenchObjective |
P14904
|
FINISHED |
| Object | to join the main French fleet for an invasion of Britain |
—
|
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: to join the main French fleet for an invasion of Britain | Statement: [Battle of Lagos, FrenchObjective, to join the main French fleet for an invasion of Britain]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: FrenchObjective Context triple: [Battle of Lagos, FrenchObjective, to join the main French fleet for an invasion of Britain]
-
A.
hasFrenchSector
Indicates that an entity includes, controls, or is associated with a sector or area designated as French.
-
B.
nameInFrench
Indicates that an entity is known or referred to by a specific name expressed in the French language.
-
C.
populationRankInFrance
Indicates the relative position of an entity in an ordered list based on its population size within France.
-
D.
equivalentTitleInFrench
Indicates that one entity’s title is the equivalent or corresponding title of another entity, specifically expressed in French.
-
E.
country1
Indicates that the subject entity is a country (or represents a country) in the given context.
- 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_69a2e7e4676c81909ea0dbdecac0687c |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ef6429e881908aa758da64299a16 |
completed | Feb. 28, 2026, 1:36 p.m. |
| PD | Predicate disambiguation | batch_69a2ede1a1108190a4a06b3416ae6156 |
completed | Feb. 28, 2026, 1:30 p.m. |
| PDg | Predicate description generation | batch_69a2ef611b9c8190ac5e9174744d9127 |
completed | Feb. 28, 2026, 1:36 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.