Triple
T8617306
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Isly |
E204071
|
entity |
| Predicate | MoroccanStrengthApprox |
P83916
|
FINISHED |
| Object | tens of thousands of troops |
—
|
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: tens of thousands of troops | Statement: [Battle of Isly, MoroccanStrengthApprox, tens of thousands of troops]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: MoroccanStrengthApprox Context triple: [Battle of Isly, MoroccanStrengthApprox, tens of thousands of troops]
-
A.
approximateStrengthFrancoSpanish
Indicates an estimated or inferred level of strength or intensity in the relationship or interaction between Franco and Spanish entities.
-
B.
approximateGallicStrength
Indicates an estimation or rough calculation of the level or magnitude of Gallic strength in a given context.
-
C.
distanceFromMarrakesh
Indicates the spatial distance between a given location and the city of Marrakesh.
-
D.
roleInMecca
Indicates that an entity holds or performs a specific role, function, or position in the context of Mecca.
-
E.
MamlukCommander
Indicates that an entity serves as a military commander within the Mamluk political or military structure in relation to another entity or 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_69ca832ceab8819096e4a9f546695079 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc4711c7748190af26ff5a78ef66a2 |
completed | March 31, 2026, 10:13 p.m. |
| PD | Predicate disambiguation | batch_69cc455437488190b7506f820daf6e32 |
completed | March 31, 2026, 10:06 p.m. |
| PDg | Predicate description generation | batch_69cc46c330bc8190a9b644078881c6ff |
completed | March 31, 2026, 10:12 p.m. |
Created at: March 30, 2026, 6:26 p.m.