Triple
T30166705
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Helena Marie O'Brien |
E766812
|
entity |
| Predicate | numberOfChildrenWithGeorgePShultz |
P202221
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [Helena Marie O'Brien, numberOfChildrenWithGeorgePShultz, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfChildrenWithGeorgePShultz Context triple: [Helena Marie O'Brien, numberOfChildrenWithGeorgePShultz, 5]
-
A.
numberOfSonsNamedGeorge
Indicates the count of an entity’s sons whose given name is George.
-
B.
numberOfCabinetMembers
Indicates the total count of cabinet members associated with a given government, administration, or leader.
-
C.
studentOfPresidentialChild
Indicates that one person is a student who is taught or mentored by a child of a president.
-
D.
numberOfTermsAsGovernor
Indicates the number of separate terms an individual has served in the role of governor.
-
E.
numberOfGovernors
Indicates the total count of governors associated with or governing a specified 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_69f2247a968881909d79c18f2bfcb275 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_6a006417d1f4819093b2da02dbf2ab22 |
completed | May 10, 2026, 10:55 a.m. |
| PD | Predicate disambiguation | batch_6a00634a7a748190ac4774493fce3d30 |
completed | May 10, 2026, 10:51 a.m. |
| PDg | Predicate description generation | batch_6a006416f78081909b6ef047c531158d |
completed | May 10, 2026, 10:55 a.m. |
Created at: April 29, 2026, 7:23 p.m.