Triple
T12957699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | HMS Nelson |
E310058
|
entity |
| Predicate | torpedoDamageEffect |
P107690
|
FINISHED |
| Object | severe structural damage |
—
|
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: severe structural damage | Statement: [HMS Nelson, torpedoDamageEffect, severe structural damage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: torpedoDamageEffect Context triple: [HMS Nelson, torpedoDamageEffect, severe structural damage]
-
A.
numberOfTorpedoesHit
Indicates the number of torpedoes that successfully struck a specified target.
-
B.
torpedoCaliber
Indicates the specific diameter or size classification of a torpedo used in a given context or system.
-
C.
numberOfTorpedoesFired
Indicates the quantity of torpedoes that have been launched or discharged in a given context or event.
-
D.
timeToSinkAfterTorpedo
Indicates the duration it takes for an entity (such as a vessel) to sink after being struck by a torpedo.
-
E.
torpedoed
Indicates that one entity attacked and struck another entity using a torpedo, typically causing damage or destruction.
- 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_69d7bdfb57a88190836b743e2825feca |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97e59a4c88190907d05b8d57dae89 |
completed | April 10, 2026, 10:48 p.m. |
| PD | Predicate disambiguation | batch_69d97dba57988190b786ffed55687a72 |
completed | April 10, 2026, 10:46 p.m. |
| PDg | Predicate description generation | batch_69d97e5811f481908178fac6d2e0efcd |
completed | April 10, 2026, 10:48 p.m. |
Created at: April 9, 2026, 5:44 p.m.