Triple
T199399
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arab League |
E4067
|
entity |
| Predicate | currentSecretaryGeneralStart |
P1056
|
FINISHED |
| Object | 2016 |
—
|
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: 2016 | Statement: [Arab League, currentSecretaryGeneralStart, 2016]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: currentSecretaryGeneralStart Context triple: [Arab League, currentSecretaryGeneralStart, 2016]
-
A.
secretaryGeneralStart
chosen
Indicates the date or point in time when an individual begins serving as secretary general of an organization or body.
-
B.
firstSecretaryGeneral
Indicates that the subject is the first person to hold the position of secretary general of the specified organization or body.
-
C.
thirdSecretaryGeneral
Indicates that the subject served as the third person to hold the position of Secretary-General in a given organization or context.
-
D.
secretaryGeneralTitle
Indicates that an entity holds the position or bears the official title of Secretary-General in relation to another entity (such as an organization or body).
-
E.
secondSecretaryGeneral
Indicates that one entity served as the second Secretary-General of the other entity (typically an organization or institution).
- 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_69a254bca59881909a15e1496f1508c7 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a25bcc6dc88190b8c24b485588dfe4 |
completed | Feb. 28, 2026, 3:06 a.m. |
| PD | Predicate disambiguation | batch_69a25b4886b48190b46fd2244648a098 |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:44 a.m.