Triple
T332200
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martha Washington |
E6648
|
entity |
| Predicate | sequenceInOffice |
P2953
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Martha Washington, sequenceInOffice, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sequenceInOffice Context triple: [Martha Washington, sequenceInOffice, 1]
-
A.
ordinalInOffice
chosen
Indicates the numerical order or rank of an individual’s term or tenure in a particular office or position.
-
B.
successorOfficeTo
Indicates that one office or position directly follows and replaces another in an official sequence or hierarchy.
-
C.
orderInOffice
Indicates that one entity holds a specific sequential position or rank within a defined term or period of holding an office or official role.
-
D.
successionOrder
Indicates the ordered sequence in which entities are designated to succeed or take over a role, position, or title.
-
E.
orderOfDeputies
Indicates the hierarchical sequence or ranking of deputies within a governing or organizational structure.
- 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_69a2e79434908190a9d5afe415153ad9 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2eab1a7048190ac690ddc2e294914 |
completed | Feb. 28, 2026, 1:16 p.m. |
| PD | Predicate disambiguation | batch_69a2e94c6d8881908239d3788c018adf |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.