Triple
T19709023
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | French-Navarrese army |
E473291
|
entity |
| Predicate | usedUnitTypes |
P28425
|
FINISHED |
| Object | infantry |
—
|
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: infantry | Statement: [French-Navarrese army, usedUnitTypes, infantry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedUnitTypes Context triple: [French-Navarrese army, usedUnitTypes, infantry]
-
A.
basedUnitType
Indicates that one unit is defined or derived in terms of another underlying (base) unit type.
-
B.
typicalSupportedUnitType
Indicates the kind or category of unit that an entity is normally designed or expected to support.
-
C.
typicalUnitType
Indicates that one entity is the standard or commonly used unit type associated with measuring or expressing the other entity.
-
D.
associatedUnitType
chosen
Indicates that one entity is linked to or characterized by a particular type or category of unit.
-
E.
supportedUnit
Indicates that one entity provides assistance, resources, or backing to another entity functioning as a unit or sub-organization.
- 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_69d8e516dd048190a0b6c93ea3e71f58 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e64406f9808190acd74aa03c392eb0 |
completed | April 20, 2026, 3:19 p.m. |
| PD | Predicate disambiguation | batch_69e530438c60819082364c7be3eef6f0 |
completed | April 19, 2026, 7:42 p.m. |
Created at: April 10, 2026, 1:46 p.m.