Triple
T26962337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ralph DLG Torres |
E679079
|
entity |
| Predicate | officeEnd_LieutenantGovernor |
P186344
|
FINISHED |
| Object | 2015-12-29 |
—
|
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: 2015-12-29 | Statement: [Ralph DLG Torres, officeEnd_LieutenantGovernor, 2015-12-29]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officeEnd_LieutenantGovernor Context triple: [Ralph DLG Torres, officeEnd_LieutenantGovernor, 2015-12-29]
-
A.
officeAssumedAsLieutenantGovernor
Indicates that an individual has formally taken on and begun serving in the role of lieutenant governor.
-
B.
hasLieutenantGovernor
Indicates that one entity serves as the lieutenant governor of another entity (typically a state, province, or territory).
-
C.
governorOfficeSeat
Indicates the location (seat) where a governor’s official office is situated.
-
D.
provinceGovernor
Indicates that one entity serves as the governor or chief administrative authority of a particular province in relation to the other entity.
-
E.
laterGovernor
Indicates that one entity subsequently became the governor of a place or jurisdiction associated with another 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_69eeeb4f3a448190b1e94b2d4776c16e |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f7cec454a88190a9f3bbee2b856636 |
completed | May 3, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69f7c8977c288190997a892ec5f756ed |
completed | May 3, 2026, 10:13 p.m. |
| PDg | Predicate description generation | batch_69f7cec398ac819081c954a993c323ee |
completed | May 3, 2026, 10:40 p.m. |
Created at: April 27, 2026, 6:32 a.m.