Triple
T21860436
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Holy Ampulla of Reims |
E539746
|
entity |
| Predicate | materialRemnants |
P44035
|
FINISHED |
| Object | fragments preserved after destruction |
—
|
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: fragments preserved after destruction | Statement: [Holy Ampulla of Reims, materialRemnants, fragments preserved after destruction]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: materialRemnants Context triple: [Holy Ampulla of Reims, materialRemnants, fragments preserved after destruction]
-
A.
remnants
chosen
Indicates that parts or traces of something remain after the main portion has been removed, used, or destroyed.
-
B.
destroyedMaterial
Indicates that one entity has caused the damage, ruin, or elimination of another entity or material.
-
C.
materialUsed
Indicates that one entity is made from, incorporates, or utilizes the other entity as its material or substance.
-
D.
material
Indicates that one entity is physically composed of, made from, or constructed using the substance or material represented by the other entity.
-
E.
materialDepicted
Indicates that a work or representation visually portrays or includes a particular material as part of its subject.
- 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_69e0c47829648190bbe2d1d7033768ec |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f0d63a22b88190b59b13e7b4788195 |
completed | April 28, 2026, 3:46 p.m. |
| PD | Predicate disambiguation | batch_69e6be9394f88190945ddd1dc004d29d |
completed | April 21, 2026, 12:02 a.m. |
Created at: April 16, 2026, 6:56 p.m.