Triple
T33716226
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nahua religion |
E863878
|
entity |
| Predicate | hasSacrificeType |
P36512
|
FINISHED |
| Object | human sacrifice |
—
|
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: human sacrifice | Statement: [Nahua religion, hasSacrificeType, human sacrifice]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSacrificeType Context triple: [Nahua religion, hasSacrificeType, human sacrifice]
-
A.
requiresSacrifice
Indicates that the fulfillment or occurrence of one entity, condition, or action depends on giving up or losing another entity, resource, or value.
-
B.
sacrificeType
chosen
Indicates the specific kind or category of sacrifice involved in a sacrificial action or relationship.
-
C.
hasRitualType
Indicates that an entity is associated with, or classified by, a particular type or category of ritual.
-
D.
sacrificedBefore
Indicates that one entity was sacrificed earlier in time than another entity.
-
E.
sacrificedTo
Indicates that one entity is offered or given up as a sacrifice in honor of, or to appease, 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_69f34989871c81908682e22a2fe4b829 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f7b0e5744c8190a22c1e1d6fcfa466 |
completed | May 3, 2026, 8:32 p.m. |
| PD | Predicate disambiguation | batch_69f7ab70d034819080295628497d8582 |
completed | May 3, 2026, 8:09 p.m. |
Created at: May 1, 2026, 1:44 a.m.