Triple
T25159917
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hope for Haiti Now telethon |
E626407
|
entity |
| Predicate | hadPhoneBankVolunteers |
P155740
|
FINISHED |
| Object | celebrities |
—
|
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: celebrities | Statement: [Hope for Haiti Now telethon, hadPhoneBankVolunteers, celebrities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadPhoneBankVolunteers Context triple: [Hope for Haiti Now telethon, hadPhoneBankVolunteers, celebrities]
-
A.
receivedVolunteersFrom
Indicates that one entity accepted or was provided with volunteers originating from another entity.
-
B.
hasVolunteerCount
Indicates the number of volunteers associated with a particular entity or activity.
-
C.
hasVolunteerPersonnelFrom
chosen
Indicates that an entity receives or utilizes volunteer personnel supplied by another entity or source.
-
D.
hasCallCenter
Indicates that an entity operates, is associated with, or is served by a call center.
-
E.
hasVolunteerParticipation
Indicates that an entity is involved in or benefits from activities performed by volunteers.
- 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_69e2ff2834ec8190b0872e2ec3d76023 |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f46b8bc80081909a48236997f4018d |
completed | May 1, 2026, 8:59 a.m. |
| PD | Predicate disambiguation | batch_69f44d8043b081908bbffd7f044b4f26 |
completed | May 1, 2026, 6:51 a.m. |
Created at: April 18, 2026, 6:31 a.m.