Triple
T15913269
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stephen Harding |
E385900
|
entity |
| Predicate | typeOfMonk |
P120509
|
FINISHED |
| Object | Benedictine monk |
—
|
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: Benedictine monk | Statement: [Stephen Harding, typeOfMonk, Benedictine monk]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfMonk Context triple: [Stephen Harding, typeOfMonk, Benedictine monk]
-
A.
wasMonkOf
Indicates that a person was a member or monk belonging to a particular religious order, monastery, or monastic community.
-
B.
numberOfMonks
Indicates the quantity or count of monks associated with a given entity or context.
-
C.
notableMonk
Indicates that a person is recognized as a monk of particular significance, prominence, or historical importance.
-
D.
monasteryType
Indicates the specific kind or classification of a monastery in relation to its broader religious or organizational category.
-
E.
typeOfBishop
Indicates that one entity is a specific kind or category of bishop in relation to another 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_69d86da686e4819097cbf3b1fc2d881d |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e172b48b308190bc430b2308cbc75b |
completed | April 16, 2026, 11:37 p.m. |
| PD | Predicate disambiguation | batch_69e142cf5c548190a931f7b58144cd31 |
completed | April 16, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69e172b213e481909ee0c05e16229a26 |
completed | April 16, 2026, 11:37 p.m. |
Created at: April 10, 2026, 4:52 a.m.