Triple
T15144553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sir George Barlow |
E361772
|
entity |
| Predicate | officeStartTime (Governor of Madras) |
P117494
|
FINISHED |
| Object | 1807 |
—
|
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: 1807 | Statement: [Sir George Barlow, officeStartTime (Governor of Madras), 1807]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officeStartTime (Governor of Madras) Context triple: [Sir George Barlow, officeStartTime (Governor of Madras), 1807]
-
A.
officeStartTime (Governor of California)
Indicates the time at which the Governor of California officially begins their term in office.
-
B.
officeStartTime (Deputy Governor of Bayelsa State)
Indicates the date and time at which the person began serving as Deputy Governor of Bayelsa State.
-
C.
officeStartTime (Governor of Bayelsa State)
Indicates the specific date and time at which an individual officially begins their tenure as Governor of Bayelsa State.
-
D.
officeStartTime (Lieutenant Governor of Pennsylvania)
Indicates the time at which the Lieutenant Governor of Pennsylvania officially begins their term in office.
-
E.
officeStartTime (Governor of Maine)
Indicates the time at which the Governor of Maine officially begins their term in office.
- 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_69d85a0759908190b8a051d2e2a1cbe6 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e005c71b688190b2e8ccfdf4db9037 |
completed | April 15, 2026, 9:40 p.m. |
| PD | Predicate disambiguation | batch_69deb9713fe881909dec2fd3f6c84b39 |
completed | April 14, 2026, 10:02 p.m. |
| PDg | Predicate description generation | batch_69dec71e8dcc81908badc834b6ccf273 |
completed | April 14, 2026, 11 p.m. |
Created at: April 10, 2026, 3:07 a.m.