Triple
T11343563
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Captain Frank Furillo |
E268659
|
entity |
| Predicate | romanticPartnerOccupation |
P99354
|
FINISHED |
| Object | public defender |
—
|
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: public defender | Statement: [Captain Frank Furillo, romanticPartnerOccupation, public defender]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: romanticPartnerOccupation Context triple: [Captain Frank Furillo, romanticPartnerOccupation, public defender]
-
A.
romanticPartnerRealName
Indicates that the real, non-alias name of a person is associated with someone who is their romantic partner.
-
B.
spouseOccupation
Indicates that one person’s spouse has a particular job, profession, or occupation.
-
C.
romanticPartnerInSpinOff
Indicates that two characters are depicted as romantic partners specifically within a spin-off work, rather than (or in addition to) the original series.
-
D.
spouseNotableWorkField
Indicates that the notable work or professional field associated with a person’s spouse is being specified.
-
E.
lifePartnerOfKeyFigureIn
Indicates that one entity is the life partner (such as spouse or long-term companion) of a key or central figure associated with another entity (such as an organization, event, or work).
- 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_69d6aacb1f0881908c84a349fd1be047 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d80148e2048190a716b515d78efdd1 |
completed | April 9, 2026, 7:43 p.m. |
| PD | Predicate disambiguation | batch_69d7e6f8aeb4819080476f16a69b2ee3 |
completed | April 9, 2026, 5:50 p.m. |
| PDg | Predicate description generation | batch_69d801451b1c8190944b17906b354142 |
completed | April 9, 2026, 7:43 p.m. |
Created at: April 8, 2026, 9:33 p.m.