Triple
T2411141
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fortified Sector of Haguenau |
E52190
|
entity |
| Predicate | hasMilitaryInfrastructure |
P22477
|
FINISHED |
| Object | underground barracks |
—
|
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: underground barracks | Statement: [Fortified Sector of Haguenau, hasMilitaryInfrastructure, underground barracks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMilitaryInfrastructure Context triple: [Fortified Sector of Haguenau, hasMilitaryInfrastructure, underground barracks]
-
A.
hasMilitaryBase
Indicates that one entity possesses, hosts, or contains a military base associated with or located on another entity.
-
B.
hasMilitaryPresence
Indicates that a military force is present in, stationed at, or operating within a particular location or entity.
-
C.
nearMilitaryInstallation
Indicates that one entity is located in close physical proximity to a military installation or facility.
-
D.
militaryInstallationType
Indicates the specific category or kind of military installation associated with an entity (e.g., base, fort, airfield, naval station).
-
E.
hasFortifications
chosen
Indicates that one entity possesses or is equipped with defensive structures or fortification works associated with it.
- 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_69ab495622948190bc6bc6e4cddaf645 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abc928fd608190885fcde6746a06bc |
completed | March 7, 2026, 6:43 a.m. |
| PD | Predicate disambiguation | batch_69abc5a6cbd0819086c0716e266b7ebb |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 6, 2026, 9:41 p.m.