Triple
T4415671
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | President of São Tomé and Príncipe |
E94964
|
entity |
| Predicate | firstOfficeholderStart |
P38523
|
FINISHED |
| Object | 1975 |
—
|
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: 1975 | Statement: [President of São Tomé and Príncipe, firstOfficeholderStart, 1975]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstOfficeholderStart Context triple: [President of São Tomé and Príncipe, firstOfficeholderStart, 1975]
-
A.
firstOfficeHolderStart
chosen
Indicates the date or time when the first person to hold a particular office or position began their term.
-
B.
firstOfficeHolder
Indicates that the subject is the very first individual to hold a particular office or position associated with the object.
-
C.
firstInOfficeTo
Indicates that one entity was the earliest or first to hold a particular office or position in relation to another entity or context.
-
D.
officeHolderStartTime
Indicates the date and time at which an individual begins holding a particular office or position.
-
E.
lastOfficeHolderStartDate
Indicates the date on which the most recent person to hold a particular office or position began their term.
- 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_69b34539638c8190abfea3eb29425210 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b354eabb2481908ad10d21e1379e7f |
completed | March 13, 2026, 12:06 a.m. |
| PD | Predicate disambiguation | batch_69b34f5d0c54819085c08533bb58030a |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:29 p.m.