Triple
T7548017
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Republic F-105 Thunderchief |
E178454
|
entity |
| Predicate | combatLosses |
P28346
|
FINISHED |
| Object | suffered heavy losses during Vietnam War operations |
—
|
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: suffered heavy losses during Vietnam War operations | Statement: [Republic F-105 Thunderchief, combatLosses, suffered heavy losses during Vietnam War operations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: combatLosses Context triple: [Republic F-105 Thunderchief, combatLosses, suffered heavy losses during Vietnam War operations]
-
A.
aircraftLosses
chosen
Indicates the number or occurrence of aircraft that have been destroyed, damaged beyond repair, or otherwise lost.
-
B.
warDamage
Indicates damage that was caused as a direct consequence of war or armed conflict.
-
C.
nativeCasualties
Indicates that native or indigenous people suffered deaths or injuries as a result of a particular event, action, or conflict.
-
D.
casualties
Indicates that an event, action, or situation resulted in people being killed or injured.
-
E.
coalitionCasualties
Indicates that members of a coalition have suffered deaths or injuries as a result of a particular conflict, event, or action.
- 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_69c69f2cbe08819088f9eb0c03ef529b |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f89b9afc8190b3e61a8e2cea7ad7 |
completed | March 27, 2026, 9:37 p.m. |
| PD | Predicate disambiguation | batch_69c6f4daad6c8190af2b8ae88d2c8cb7 |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:49 p.m.