Triple
T31534891
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hi Jolly Cemetery |
E804577
|
entity |
| Predicate | materialOfMainMonument |
P1272
|
FINISHED |
| Object | local stone |
—
|
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: local stone | Statement: [Hi Jolly Cemetery, materialOfMainMonument, local stone]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: materialOfMainMonument Context triple: [Hi Jolly Cemetery, materialOfMainMonument, local stone]
-
A.
mainAltarMaterial
Indicates the material from which the main altar is made.
-
B.
statueMaterialDetail
Indicates that a statue is made of, or incorporates, a specific material with detailed characterization (e.g., type, composition, or quality).
-
C.
materialUsed
chosen
Indicates that one entity is made from, incorporates, or utilizes the other entity as its material or substance.
-
D.
templeMaterial
Indicates that a temple is constructed from, or primarily composed of, a specified material.
-
E.
materialOfFortifications
Indicates that a specified material is used in the construction or composition of certain fortifications.
- 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_69f348d03ef88190a2b73d7b94b9e02d |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6fb19063c81909466b329655c8583 |
completed | May 3, 2026, 7:36 a.m. |
| PD | Predicate disambiguation | batch_69f6f969b4cc8190afb473a2d8b110bc |
completed | May 3, 2026, 7:29 a.m. |
Created at: April 30, 2026, 10:03 p.m.