Triple
T11474868
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Et in terra pax |
E271999
|
entity |
| Predicate | hasTextOpeningWords |
P2827
|
FINISHED |
| Object | Et in terra pax hominibus bonae voluntatis |
—
|
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: Et in terra pax hominibus bonae voluntatis | Statement: [Et in terra pax, hasTextOpeningWords, Et in terra pax hominibus bonae voluntatis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTextOpeningWords Context triple: [Et in terra pax, hasTextOpeningWords, Et in terra pax hominibus bonae voluntatis]
-
A.
hasTextOpening
chosen
Indicates that an entity begins with or contains a specified initial segment of text.
-
B.
hasOpening
Indicates that one entity possesses or features an opening, gap, or entrance that allows access, passage, or exposure.
-
C.
hasText
Indicates that an entity is associated with or contains a specific piece of textual content.
-
D.
hasOpeningType
Indicates that one entity has, features, or is characterized by a particular type or kind of opening.
-
E.
hasOpeningSetting
Indicates that one entity (typically a narrative work) has its initial scene or setting located in the other entity.
- 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_69d6aae0c8d881908a5a360c0be3242e |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8294c8dc48190a515f83c99405a3b |
completed | April 9, 2026, 10:33 p.m. |
| PD | Predicate disambiguation | batch_69d8086ecd6c81908f424864857762d6 |
completed | April 9, 2026, 8:13 p.m. |
Created at: April 8, 2026, 9:36 p.m.