Triple
T5530568
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lady (title) |
E145036
|
entity |
| Predicate | usedForRank |
P98
|
FINISHED |
| Object | marquess’s wife |
—
|
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: marquess’s wife | Statement: [Lady (title), usedForRank, marquess’s wife]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usedForRank Context triple: [Lady (title), usedForRank, marquess’s wife]
-
A.
usesRank
Indicates that one entity applies or relies on a ranking or ordered level system associated with another entity.
-
B.
usedFor
chosen
Indicates that one entity serves a purpose, function, or role in accomplishing, enabling, or supporting another entity or activity.
-
C.
usesRankStructure
Indicates that an entity organizes its members or components according to a defined hierarchical rank structure.
-
D.
scoreUsedFor
Indicates that a particular score or rating is used for a specific purpose, decision, or downstream process.
-
E.
hasRankingFactor
Indicates that one entity contributes as a factor to determining the ranking or ordered position of another entity.
- 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_69c008f9955881909bfa8348b56b4739 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f9b59bc8190a8758b3be54831e9 |
completed | March 22, 2026, 4:58 p.m. |
| PD | Predicate disambiguation | batch_69c01b0c50e48190a1b03ecd20ca440b |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:34 p.m.