Triple
T742557
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stations of the Cross |
E15273
|
entity |
| Predicate | sixthStation |
P18882
|
FINISHED |
| Object | Veronica wipes the face of Jesus |
—
|
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: Veronica wipes the face of Jesus | Statement: [Stations of the Cross, sixthStation, Veronica wipes the face of Jesus]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sixthStation Context triple: [Stations of the Cross, sixthStation, Veronica wipes the face of Jesus]
-
A.
junctionStation
Indicates that a station functions as a junction where multiple routes or lines intersect or connect.
-
B.
primaryStation
Indicates that one station is designated as the main or principal station associated with another entity or within a given context.
-
C.
interchangeStation
Indicates a station where passengers can transfer between different routes, lines, or modes of transportation.
-
D.
sisterStation
Indicates that two broadcast stations are related as counterparts, typically serving different areas or platforms under common ownership or affiliation.
-
E.
stationName
Indicates the name assigned to a particular station in the relationship.
- 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_69a49358aa308190adbc9b5a0a2adcf9 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a60f92d08190a4f44c5b4d068ab5 |
completed | March 1, 2026, 8:48 p.m. |
| PD | Predicate disambiguation | batch_69a4a4fdaaf48190985f62acfc069508 |
completed | March 1, 2026, 8:43 p.m. |
| PDg | Predicate description generation | batch_69a4a5a35c68819082429755c046e9a7 |
completed | March 1, 2026, 8:46 p.m. |
Created at: March 1, 2026, 7:37 p.m.