Triple
T15068016
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BB-62 |
E379802
|
entity |
| Predicate | hasCrewComplement |
P30694
|
FINISHED |
| Object | approximately 1,900 in World War II |
—
|
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: approximately 1,900 in World War II | Statement: [BB-62, hasCrewComplement, approximately 1,900 in World War II]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCrewComplement Context triple: [BB-62, hasCrewComplement, approximately 1,900 in World War II]
-
A.
hasCrewCapacity
Indicates that an entity is capable of accommodating a specified number of crew members.
-
B.
crewOnboard
Indicates that a person or group is serving as crew aboard a specific vehicle, vessel, or craft.
-
C.
crewCount
chosen
Indicates the number of crew members associated with an entity, such as a vehicle, vessel, or mission.
-
D.
crewComplementType
Indicates the classification or category of a crew complement associated with an entity (such as its role, composition, or staffing type).
-
E.
totalCrewMembers
Indicates the total number of crew members associated with a given entity or context.
- 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_69d85cd7683881908d405c1b5d7b4f7f |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69dedeebc7e48190a86b4f0afe8844bb |
completed | April 15, 2026, 12:42 a.m. |
| PD | Predicate disambiguation | batch_69deb95a182081908fffc4402b02a394 |
completed | April 14, 2026, 10:02 p.m. |
Created at: April 10, 2026, 3:02 a.m.