Triple
T10187883
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SMS König |
E236956
|
entity |
| Predicate | salvageHistory |
P47246
|
FINISHED |
| Object | partially salvaged in 1960s–1970s |
—
|
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: partially salvaged in 1960s–1970s | Statement: [SMS König, salvageHistory, partially salvaged in 1960s–1970s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: salvageHistory Context triple: [SMS König, salvageHistory, partially salvaged in 1960s–1970s]
-
A.
wreckSalvaged
chosen
Indicates that a previously wrecked object or structure has been recovered or salvaged from its damaged or sunken state.
-
B.
hasShipwrecks
Indicates that one entity contains, includes, or is associated with shipwrecks located within it or under its control.
-
C.
suspensionHistory
Indicates a record of past instances in which an entity was suspended, including when and possibly why those suspensions occurred.
-
D.
savedFromDemolition
Indicates that one entity prevented another entity (typically a structure or site) from being destroyed or torn down.
-
E.
ownershipHistory
Indicates the sequence of past and present owners associated with an entity over time.
- 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_69ca84d7260c8190bfbec36762943f37 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cded7a7aac8190af8dcb8374e62d68 |
completed | April 2, 2026, 4:15 a.m. |
| PD | Predicate disambiguation | batch_69cd7c8477648190bc55c56aeec507d3 |
completed | April 1, 2026, 8:13 p.m. |
Created at: March 30, 2026, 9:12 p.m.