Triple
T948002
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Suribachi |
E20455
|
entity |
| Predicate | hasFortifications |
P22477
|
FINISHED |
| Object | Japanese defensive positions in WWII |
—
|
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: Japanese defensive positions in WWII | Statement: [Mount Suribachi, hasFortifications, Japanese defensive positions in WWII]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFortifications Context triple: [Mount Suribachi, hasFortifications, Japanese defensive positions in WWII]
-
A.
isFortifiedCity
Indicates that a city is strengthened with defensive structures or fortifications, such as walls, ramparts, or similar protective works.
-
B.
containsFortress
Indicates that a location or area includes a fortress within its boundaries.
-
C.
fortressBuiltUnder
Indicates that one structure or location was constructed directly beneath or within the protective area of a fortress.
-
D.
hasMoat
Indicates that one entity is surrounded or protected by a moat associated with another entity.
-
E.
hasMilitaryBase
Indicates that one entity possesses, hosts, or contains a military base associated with or located on another entity.
- 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_69a493b0f2fc81908cd227480a5356a1 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b3be19448190a8863fce3d152430 |
completed | March 1, 2026, 9:46 p.m. |
| PD | Predicate disambiguation | batch_69a4b29f05f481908814bd11f235e9d0 |
completed | March 1, 2026, 9:41 p.m. |
| PDg | Predicate description generation | batch_69a4b385176081909e3e8c3f647c1fd4 |
completed | March 1, 2026, 9:45 p.m. |
Created at: March 1, 2026, 7:40 p.m.