Triple
T24457983
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beatitudes |
E616740
|
entity |
| Predicate | numberOfBeatitudesInMatthew |
P156187
|
FINISHED |
| Object | 8 or 9 (depending on counting) |
—
|
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: 8 or 9 (depending on counting) | Statement: [Beatitudes, numberOfBeatitudesInMatthew, 8 or 9 (depending on counting)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfBeatitudesInMatthew Context triple: [Beatitudes, numberOfBeatitudesInMatthew, 8 or 9 (depending on counting)]
-
A.
numberOfCommandments
Indicates the total count of commandments associated with a given subject.
-
B.
numberInBenedictEncyclicals
Indicates the count of times a given entity is mentioned or appears within the encyclicals authored by Pope Benedict.
-
C.
numberOfCanonicalGospels
Indicates the count of canonical gospels associated with a given religious tradition or context.
-
D.
originalNumberOfBlessings
Indicates the initial total count of blessings associated with an entity before any changes or adjustments occur.
-
E.
numberOfSutras
Indicates the quantity or count of sutras associated with a given entity.
- F. None of above. chosen
Provenance (4 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_69e2d7ef9fe08190a0613908758b4e86 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f298c812bc8190969836ee8f0eb2f2 |
completed | April 29, 2026, 11:48 p.m. |
| PD | Predicate disambiguation | batch_69f287d3237c819099559c00f83131d8 |
completed | April 29, 2026, 10:36 p.m. |
| PDg | Predicate description generation | batch_69f28f4d978c81908310c01def2514cc |
completed | April 29, 2026, 11:07 p.m. |
Created at: April 18, 2026, 2:19 a.m.