Triple
T12284138
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Inti Raymi |
E292784
|
entity |
| Predicate | hasCostumes |
P104023
|
FINISHED |
| Object | Inca-style ceremonial dress |
—
|
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: Inca-style ceremonial dress | Statement: [Inti Raymi, hasCostumes, Inca-style ceremonial dress]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCostumes Context triple: [Inti Raymi, hasCostumes, Inca-style ceremonial dress]
-
A.
haveDistinctCostume
Indicates that the entities each possess a costume that is different from the others’ costumes.
-
B.
numberOfCostumes
Indicates the total count of costumes associated with or used by a given entity.
-
C.
colorAssociatedWithCostume
Indicates that a particular color is thematically or typically linked to a specific costume.
-
D.
designedCostumesFor
Indicates that one entity created or planned the costumes used by another entity, typically for a performance, production, or event.
-
E.
hasLabelOnCostume
Indicates that a costume bears a specific label or tag attached to it.
- 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_69d6ab690ad081908c0ed3870ec82d53 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d9261e1570819084bb4fdb44aa6aea |
completed | April 10, 2026, 4:32 p.m. |
| PD | Predicate disambiguation | batch_69d91c4d9a9c8190aeb7beaf9792d8f0 |
completed | April 10, 2026, 3:50 p.m. |
| PDg | Predicate description generation | batch_69d9261b7f088190b69fe6961015fce3 |
completed | April 10, 2026, 4:32 p.m. |
Created at: April 8, 2026, 9:52 p.m.