Triple
T8717429
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Commander, U.S. 6th Fleet |
E206929
|
entity |
| Predicate | typicalTenure |
P51579
|
FINISHED |
| Object | approximately two to three years |
—
|
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 two to three years | Statement: [Commander, U.S. 6th Fleet, typicalTenure, approximately two to three years]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalTenure Context triple: [Commander, U.S. 6th Fleet, typicalTenure, approximately two to three years]
-
A.
tenureType
Indicates the type or category of tenure or contractual engagement that characterizes the relationship between the involved entities.
-
B.
tenureCharacteristic
chosen
Indicates a relationship where a specific attribute or quality characterizes the duration or conditions of someone’s or something’s tenure.
-
C.
teamTenure
Indicates the duration or length of time an entity has been part of a particular team.
-
D.
lifeTenure
Indicates that an individual holds a position or office for the duration of their lifetime, without a fixed term limit or routine reappointment.
-
E.
occupationDuration
Indicates the length of time an entity holds or has held a particular occupation or role.
- 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_69ca83572d4881909bef3be2b578d539 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5cdac6988190b9f9cc1f350aae53 |
completed | March 31, 2026, 11:46 p.m. |
| PD | Predicate disambiguation | batch_69cc456e806c819087e7d66ee737f242 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:36 p.m.