Triple
T2202147
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baker |
E50513
|
entity |
| Predicate | effectOnShips |
P36949
|
FINISHED |
| Object | shock damage from underwater blast |
—
|
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: shock damage from underwater blast | Statement: [Baker, effectOnShips, shock damage from underwater blast]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnShips Context triple: [Baker, effectOnShips, shock damage from underwater blast]
-
A.
numberOfShipsInvolved
Indicates the total count of ships that participated or were involved in a specified event or situation.
-
B.
typicalShipStrength
Indicates the usual or characteristic level of strength or power associated with a given ship.
-
C.
navalFeatures
Indicates that something possesses characteristics, components, or attributes specifically associated with naval or maritime contexts.
-
D.
statusOfOtherShips
Indicates the relationship that reports or reflects the current conditions or states (such as position, readiness, or operational status) of other ships.
-
E.
navalFleet
Indicates a relationship where multiple naval vessels are organized and operate together as a coordinated maritime military force.
- 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_69a88b044ab48190add007487680f009 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abbfa1b41c8190b0f7467d0dcdfbcd |
completed | March 7, 2026, 6:03 a.m. |
| PD | Predicate disambiguation | batch_69abbda706f4819094de73e1d1d1f539 |
completed | March 7, 2026, 5:54 a.m. |
| PDg | Predicate description generation | batch_69abbf35c994819088a093c412931de4 |
completed | March 7, 2026, 6:01 a.m. |
Created at: March 4, 2026, 7:46 p.m.