Triple
T140908
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Operation Torch |
E2846
|
entity |
| Predicate | planningCodeName |
P2980
|
FINISHED |
| Object | Gymnast |
—
|
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: Gymnast | Statement: [Operation Torch, planningCodeName, Gymnast]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: planningCodeName Context triple: [Operation Torch, planningCodeName, Gymnast]
-
A.
operationCodeName
chosen
Indicates that an operation is associated with a specific code name used to identify or refer to it.
-
B.
plannedFacility
Indicates that a facility is intended or scheduled to be built, established, or implemented in the future but does not yet exist or operate.
-
C.
codenameUser
Indicates that a user is assigned or associated with a particular codename.
-
D.
codeFor
Indicates that one entity serves as the implementation, encoding, or programmatic representation for another entity.
-
E.
codeNameOfGermanInvasionPlan
Indicates that one entity is the codename assigned to a specific German invasion plan involving the other entity.
- 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_69a2521e35c08190b28e5c9f1e3c9b59 |
completed | Feb. 28, 2026, 2:25 a.m. |
| NER | Named-entity recognition | batch_69a2580ca15481909fa3e87d804a1b23 |
completed | Feb. 28, 2026, 2:50 a.m. |
| PD | Predicate disambiguation | batch_69a2565559ac81909e0c4e095a7dfa27 |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.