Triple
T31976685
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Meuse crossings |
E816467
|
entity |
| Predicate | defensiveWeakness |
P47330
|
FINISHED |
| Object | insufficient French air support |
—
|
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: insufficient French air support | Statement: [Meuse crossings, defensiveWeakness, insufficient French air support]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: defensiveWeakness Context triple: [Meuse crossings, defensiveWeakness, insufficient French air support]
-
A.
hasWeakness
chosen
Indicates that one entity is vulnerable to, or can be adversely affected or defeated by, another entity.
-
B.
defensiveSkillEmphasized
Indicates that particular focus or priority is placed on developing or utilizing defensive skills in the relevant context.
-
C.
defensiveSide
Indicates which entity is acting in a defensive role or position relative to another entity or situation.
-
D.
defensiveConcern
Indicates a relationship where one entity is worried about protecting itself or another from potential harm, threat, or criticism.
-
E.
playstyleWeakness
Indicates a relationship where one playstyle is particularly vulnerable or disadvantaged when facing another playstyle.
- 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_69f348f6a3008190bfb59ca695fd68e2 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f739a638748190808e7a2930dce16e |
completed | May 3, 2026, 12:03 p.m. |
| PD | Predicate disambiguation | batch_69f732f2dc6c8190a4e86da98cc5eb05 |
completed | May 3, 2026, 11:35 a.m. |
Created at: May 1, 2026, 12:11 a.m.