Triple
T27789396
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Overseas Filipino communities |
E701039
|
entity |
| Predicate | hasCommonProfession |
P136099
|
FINISHED |
| Object | nurse |
—
|
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: nurse | Statement: [Overseas Filipino communities, hasCommonProfession, nurse]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCommonProfession Context triple: [Overseas Filipino communities, hasCommonProfession, nurse]
-
A.
isCommonInProfession
Indicates that something frequently occurs, appears, or is typical within a given profession or occupational field.
-
B.
hasCoOwnerProfession
Indicates that two or more co-owners share a specified profession or occupational role in relation to the same owned entity.
-
C.
hasChildInSameProfession
Indicates that an individual has at least one child whose profession is the same as their own.
-
D.
sharesProfessionWith
Indicates that two entities have the same profession or occupational role.
-
E.
commonProfessionAmongBearers
chosen
Indicates that multiple entities sharing a given attribute (such as a name or title) are frequently associated with the same profession.
- 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_69ef6a50d8088190acbf3dfbb06d8091 |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69ff45793d5c81909dc503ad1f714ee2 |
completed | May 9, 2026, 2:32 p.m. |
| PD | Predicate disambiguation | batch_69ff41cb0e088190a6e9b03cb20e5fad |
completed | May 9, 2026, 2:16 p.m. |
Created at: April 27, 2026, 5:26 p.m.