Triple
T9625111
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Memorial in Winchester Cathedral |
E232440
|
entity |
| Predicate | hasSubjectRank |
P11443
|
FINISHED |
| Object | British Army 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: British Army officer | Statement: [Memorial in Winchester Cathedral, hasSubjectRank, British Army officer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSubjectRank Context triple: [Memorial in Winchester Cathedral, hasSubjectRank, British Army officer]
-
A.
hasSubjectPosition
Indicates that an entity occupies or is assigned to a particular subject role or position within a structure, context, or organization.
-
B.
hasTypicalSubject
Indicates that something is commonly or characteristically used as the subject (agent or topic) of a given relation or action.
-
C.
usesRank
Indicates that one entity applies or relies on a ranking or ordered level system associated with another entity.
-
D.
hasRankCategory
chosen
Indicates that an entity is assigned to a particular rank-based classification or level within an ordered hierarchy.
-
E.
hasSubjectThesaurus
Indicates that an entity is associated with or described using a particular subject thesaurus or controlled subject vocabulary.
- 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_69ca848793ec8190a93a12383a754dc0 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9afb67c88190aa170716f0033752 |
completed | April 1, 2026, 10:23 p.m. |
| PD | Predicate disambiguation | batch_69ccd5acfa5c8190aaba3cf548723604 |
completed | April 1, 2026, 8:22 a.m. |
Created at: March 30, 2026, 8:10 p.m.