Triple
T34698937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nyangatom people |
E1000306
|
entity |
| Predicate | usesBodyModification |
P163108
|
FINISHED |
| Object | scarification for identity and status |
—
|
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: scarification for identity and status | Statement: [Nyangatom people, usesBodyModification, scarification for identity and status]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesBodyModification Context triple: [Nyangatom people, usesBodyModification, scarification for identity and status]
-
A.
usesBody
Indicates that one entity employs or utilizes the physical body of another (or its own) as a means or instrument to perform an action or function.
-
B.
modifiesBody
chosen
Indicates that one entity alters, changes, or affects the physical body or bodily state of another entity.
-
C.
usesBodyPart
Indicates that an entity performs an action or function by employing a specific body part as a means or tool.
-
D.
bodyTransformation
Indicates a change in an entity’s physical form, structure, or appearance into a different bodily state.
-
E.
bodyModificationReason
Indicates the reason or motivation behind a particular body modification performed on an 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_69f76dab937881909c86f1b9ad50445f |
completed | May 3, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f7805ce6208190ac6dbd9c97989978 |
completed | May 3, 2026, 5:05 p.m. |
| PD | Predicate disambiguation | batch_69f77956ec648190ba4fb7e9d83fd107 |
completed | May 3, 2026, 4:35 p.m. |
Created at: May 3, 2026, 3:59 p.m.