Triple
T25909348
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | الغيداق بن عبد المطلب |
E652845
|
entity |
| Predicate | عاش_في |
P18931
|
FINISHED |
| Object | مكة |
—
|
NE NERFINISHED |
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: مكة | Statement: [الغيداق بن عبد المطلب, عاش_في, مكة]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: عاش_في Context triple: [الغيداق بن عبد المطلب, عاش_في, مكة]
-
A.
spentMostOfLifeIn
chosen
Indicates that an entity resided or was primarily based in a particular place for the majority of its lifetime.
-
B.
survivesIn
Indicates that an entity remains alive, functional, or intact within a specified environment, condition, or context.
-
C.
didNotLiveToSee
Indicates that one entity died before a particular event or state involving another entity occurred, and therefore never witnessed it.
-
D.
eatenAndResurrectedIn
Indicates that an entity was eaten in a particular place or context and later brought back to life or restored there.
-
E.
laterLivedIn
Indicates that one entity resided in a particular place during a later period of its life, after an earlier residence elsewhere.
- 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_69e7ab3d3f8481909bc53ed64c06af33 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f603c2ece48190812532cb235714ad |
completed | May 2, 2026, 2:01 p.m. |
| PD | Predicate disambiguation | batch_69f5afec3e94819080d9ba86cf8c866e |
completed | May 2, 2026, 8:03 a.m. |
Created at: April 22, 2026, 8:28 a.m.