Triple
T23074424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Salome Violetta Haertel |
E575288
|
entity |
| Predicate | hasAttended |
P150851
|
FINISHED |
| Object | fan events |
—
|
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: fan events | Statement: [Salome Violetta Haertel, hasAttended, fan events]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAttended Context triple: [Salome Violetta Haertel, hasAttended, fan events]
-
A.
hasTypicalAttendance
Indicates the usual or characteristic number of attendees associated with an event, venue, or activity.
-
B.
laterAttends
Indicates that one entity attends an event or place at a time later than another referenced attendance.
-
C.
hasAttendanceType
Indicates the specific category or mode of attendance associated with an event or participant (e.g., in-person, virtual, hybrid).
-
D.
brieflyAttended
Indicates that an entity was present at or participated in another entity or event for only a short or limited duration.
-
E.
attendanceAnnounced
Indicates that an official statement has been made about whether and/or how many people will attend an event.
- 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_69e245be28d48190ad1348d5a73db37d |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18c61bb7c8190a3d9b1fba173cdff |
completed | April 29, 2026, 4:43 a.m. |
| PD | Predicate disambiguation | batch_69ef89e5ce748190b2c3ac3843484127 |
completed | April 27, 2026, 4:08 p.m. |
| PDg | Predicate description generation | batch_69ef9b7494f4819088ae59ea3d0ae8ab |
completed | April 27, 2026, 5:23 p.m. |
Created at: April 17, 2026, 3:56 p.m.