Triple
T1640933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Junior Deacon |
E35468
|
entity |
| Predicate | wornJewelry |
P271
|
FINISHED |
| Object | officer’s jewel |
—
|
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: officer’s jewel | Statement: [Junior Deacon, wornJewelry, officer’s jewel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wornJewelry Context triple: [Junior Deacon, wornJewelry, officer’s jewel]
-
A.
wornAround
Indicates that one entity is physically worn encircling or surrounding another entity (e.g., around a body part or object).
-
B.
wearClassification
Indicates a classification relationship specifying the type or category of wear associated with an entity or interaction.
-
C.
wears
chosen
Indicates that one entity is dressed in, or has on its body, a particular item such as clothing or accessories.
-
D.
associatedMetal
Indicates a relationship where one entity is linked or connected to a particular metal, such as by composition, usage, origin, or symbolic association.
-
E.
wearLocation
Indicates the typical body part or location on which an item is worn.
- 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_69a88604618c81908b41f6429c431eb6 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a919306fd48190a245fc95e0e759d9 |
completed | March 5, 2026, 5:48 a.m. |
| PD | Predicate disambiguation | batch_69a907cc9d348190b76b0d3f596e5a81 |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:28 p.m.