Triple
T33683631
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nimitz family |
E862967
|
entity |
| Predicate | hasNotableOccupationTradition |
P104544
|
FINISHED |
| Object | naval officer |
—
|
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: naval officer | Statement: [Nimitz family, hasNotableOccupationTradition, naval officer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableOccupationTradition Context triple: [Nimitz family, hasNotableOccupationTradition, naval officer]
-
A.
hasOccupationTradition
chosen
Indicates that an entity is associated with a customary or historically established occupation or professional role.
-
B.
hasNotableProfessionDistributionIn
Indicates that the distribution or prevalence of notable professions associated with an entity is observed or characterized within a specified context, such as a location or group.
-
C.
hasNotableProfessionField
Indicates that an entity’s notable profession or occupation belongs to a particular professional field or domain.
-
D.
hasNotableBearerOccupation
Indicates that an entity is associated with a notable person who holds a specific occupation.
-
E.
hasNotableProfessionalDetails
Indicates that there are significant or noteworthy aspects about an entity’s professional background, career, or work history.
- 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_69f3498662b48190904442c39df84fb7 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a01d7b3ce8c8190b2f90be730505765 |
completed | May 11, 2026, 1:20 p.m. |
| PD | Predicate disambiguation | batch_6a01d5115a6c8190a6d9f96ec484135a |
completed | May 11, 2026, 1:09 p.m. |
Created at: May 1, 2026, 1:43 a.m.