Triple
T1363393
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 13B Cannon Crewmember |
E29146
|
entity |
| Predicate | typicalRankRange |
P27475
|
FINISHED |
| Object | E-1 to E-7 |
—
|
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: E-1 to E-7 | Statement: [13B Cannon Crewmember, typicalRankRange, E-1 to E-7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalRankRange Context triple: [13B Cannon Crewmember, typicalRankRange, E-1 to E-7]
-
A.
typicalRank
Indicates the usual or most common rank or position an entity holds within a given ordering or hierarchy.
-
B.
tensorRank
Indicates the rank (number of indices or dimensions) associated with a given tensor.
-
C.
dimensionCount
Indicates the number of distinct dimensions or axes associated with an entity or data structure.
-
D.
hasRankOrder
Indicates that one entity is ordered or positioned relative to others according to a specific ranking or sequence.
-
E.
depthRank
Indicates the relative ordering of entities based on how deep or distant they are along a specified depth dimension or hierarchy.
- 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_69a498d77abc8190913bf57e5f51d2c4 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c2b5d05c81908b49e282648e073a |
completed | March 1, 2026, 10:50 p.m. |
| PD | Predicate disambiguation | batch_69a4bef945c08190a027472fdd695ea5 |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4c06721488190ac7f6e012f21af3d |
completed | March 1, 2026, 10:40 p.m. |
Created at: March 1, 2026, 7:57 p.m.