Triple
T29522229
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Capela dos Ossos |
E748965
|
entity |
| Predicate | numberOfHumanRemains |
P156012
|
FINISHED |
| Object | thousands of skeletons |
—
|
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: thousands of skeletons | Statement: [Capela dos Ossos, numberOfHumanRemains, thousands of skeletons]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfHumanRemains Context triple: [Capela dos Ossos, numberOfHumanRemains, thousands of skeletons]
-
A.
numberOfHumanRemainsFound
chosen
Indicates the quantity of human remains that have been discovered in a given context or location.
-
B.
numberOfUnidentifiedRemains
Indicates the count of human remains that have not yet been identified in a given context or case.
-
C.
numberOfUnidentifiedBurials
Indicates the count of burial sites or graves where the interred individuals have not been identified.
-
D.
presentUseOfRemains
Indicates that the remains of an entity are currently being used or utilized in some manner.
-
E.
bodiesExhumed
Indicates that previously buried bodies have been dug up and removed from their place of interment.
- 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_69f0bd46d99c81908ba9d01cc1dbef7d |
completed | April 28, 2026, 1:59 p.m. |
| NER | Named-entity recognition | batch_69ffff262fac8190a26ed0577e1860bd |
completed | May 10, 2026, 3:44 a.m. |
| PD | Predicate disambiguation | batch_69fffcad7d7c8190a4ad7bb35e33bb75 |
completed | May 10, 2026, 3:34 a.m. |
Created at: April 28, 2026, 4:42 p.m.