Triple
T38126287
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margaret Beaudine |
E952080
|
entity |
| Predicate | spouseNotableWorkLocation |
P198131
|
FINISHED |
| Object | Hollywood |
—
|
NE NERFINISHED |
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: Hollywood | Statement: [Margaret Beaudine, spouseNotableWorkLocation, Hollywood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseNotableWorkLocation Context triple: [Margaret Beaudine, spouseNotableWorkLocation, Hollywood]
-
A.
spouseNotableWorkField
Indicates that the notable work or professional field associated with a person’s spouse is being specified.
-
B.
spouse notableWork
Indicates that a person's spouse is significantly associated with a particular notable work.
-
C.
spousePoliticalActivityLocation
Indicates the place where a spouse’s political activities or engagements occur.
-
D.
spouseNotableFor
Indicates that a person's spouse is recognized or distinguished for a particular achievement, role, or characteristic.
-
E.
spousePlaceOfWork
Indicates that the place of work specified belongs to the spouse of the referenced person.
- 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_69f76f083548819082bd2bbf53c79e8e |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fecb4d02f881909a9ee97ce98000d5 |
completed | May 9, 2026, 5:51 a.m. |
| PD | Predicate disambiguation | batch_69fec9846c1c8190b317f0711f0755db |
completed | May 9, 2026, 5:43 a.m. |
| PDg | Predicate description generation | batch_69fecb4be24481908bda6197d7fc32f4 |
completed | May 9, 2026, 5:51 a.m. |
Created at: May 3, 2026, 4:21 p.m.