Triple
T5035758
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tallboy bomb |
E113415
|
entity |
| Predicate | roleInSinking |
P4687
|
FINISHED |
| Object | helped sink the German battleship Tirpitz |
—
|
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: helped sink the German battleship Tirpitz | Statement: [Tallboy bomb, roleInSinking, helped sink the German battleship Tirpitz]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInSinking Context triple: [Tallboy bomb, roleInSinking, helped sink the German battleship Tirpitz]
-
A.
opposingForcesAtSinking
Indicates that two or more forces are acting in opposition to each other at the time or location of a sinking event.
-
B.
sunkBy
chosen
Indicates that one entity (typically a vessel or structure) was caused to sink or be destroyed in water by another entity.
-
C.
sunkDuring
Indicates that one entity was sunk in the course of, or as a result of, the event or time period represented by another entity.
-
D.
sankOnMaidenVoyage
Indicates that the subject vessel sank during its very first voyage.
-
E.
sankOn
Indicates that one entity moved downward and became submerged or lower in level relative to another entity or reference point.
- 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_69bd44384298819089c49e7c330ec7b8 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd73b9ad488190a2a8c4da8858eb91 |
completed | March 20, 2026, 4:20 p.m. |
| PD | Predicate disambiguation | batch_69bd71509e9c8190a60c1d8d04936a12 |
completed | March 20, 2026, 4:09 p.m. |
Created at: March 20, 2026, 1:36 p.m.