Triple
T560688
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fat Man |
E13442
|
entity |
| Predicate | weaponCategory |
P16410
|
FINISHED |
| Object | weapon of mass destruction |
—
|
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: weapon of mass destruction | Statement: [Fat Man, weaponCategory, weapon of mass destruction]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: weaponCategory Context triple: [Fat Man, weaponCategory, weapon of mass destruction]
-
A.
weapon
Indicates that one entity is used as a weapon by, or serves as the weapon of, another entity.
-
B.
weaponsUsed
Indicates that one entity employed or utilized another entity as a weapon in carrying out an action or event.
-
C.
typicalWeapon
Indicates that the object is a weapon commonly or characteristically used by the subject.
-
D.
gunType
Indicates the specific category or kind of gun associated with an entity.
-
E.
weaponTypeTested
Indicates that a specific type of weapon has been subjected to a test or evaluation in the described context.
- 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_69a4933edcf08190b35ecfd6014caee6 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a499e2795c8190903240e79964156d |
completed | March 1, 2026, 7:56 p.m. |
| PD | Predicate disambiguation | batch_69a494befb8481908bb4e2e9f31e343b |
completed | March 1, 2026, 7:34 p.m. |
| PDg | Predicate description generation | batch_69a4985952a481908b918350ececf484 |
completed | March 1, 2026, 7:49 p.m. |
Created at: March 1, 2026, 7:32 p.m.