Triple
T10553350
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beth Haim Jewish cemetery |
E249009
|
entity |
| Predicate | materialUsedForTombstones |
P66449
|
FINISHED |
| Object | marble |
—
|
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: marble | Statement: [Beth Haim Jewish cemetery, materialUsedForTombstones, marble]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: materialUsedForTombstones Context triple: [Beth Haim Jewish cemetery, materialUsedForTombstones, marble]
-
A.
hasGraveMarkersMaterial
chosen
Indicates that the material composition of grave markers is a specified substance or type.
-
B.
hasSarcophagusMaterial
Indicates that a sarcophagus is made from, or primarily composed of, a specified material.
-
C.
materialFromQuarriesUsedFor
Indicates that material extracted from quarries is used for a particular purpose, project, or object.
-
D.
materialUsed
Indicates that one entity is made from, incorporates, or utilizes the other entity as its material or substance.
-
E.
usesMaterialForMounds
Indicates that an entity constructs or forms mounds using a specified material.
- 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_69d381c733c08190ab1dd6239f5f34ae |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d52710869c81909b6db1a190825bad |
completed | April 7, 2026, 3:47 p.m. |
| PD | Predicate disambiguation | batch_69d518fa0b4081909bffc936d78bd77b |
completed | April 7, 2026, 2:47 p.m. |
Created at: April 6, 2026, 12:34 p.m.