Triple
T31359307
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dulcinea del Toboso |
E799819
|
entity |
| Predicate | hasOccupationInReality |
P171664
|
FINISHED |
| Object | farm girl |
—
|
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: farm girl | Statement: [Dulcinea del Toboso, hasOccupationInReality, farm girl]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOccupationInReality Context triple: [Dulcinea del Toboso, hasOccupationInReality, farm girl]
-
A.
hasOccupationDuringStory
Indicates that an entity holds or performs a particular occupation or job role during the time span covered by the story.
-
B.
hasTypicalOccupation
Indicates that an entity commonly or characteristically works in a particular job or profession.
-
C.
hasOccupationInWork
Indicates that an entity holds or performs a specific occupation within a particular work, project, or creative production.
-
D.
hasGivenProfession
Indicates that an entity holds or practices a specified profession or occupation.
-
E.
hasHumanOccupationEvidence
Indicates that there is supporting evidence that a human has held or currently holds a particular occupation or job role.
- 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_69f224e5e9bc8190a16339328897c4f8 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f6a1ac56b88190a820434b65c9fa23 |
completed | May 3, 2026, 1:15 a.m. |
| PD | Predicate disambiguation | batch_69f69fe463248190aa78128abeab1183 |
completed | May 3, 2026, 1:07 a.m. |
| PDg | Predicate description generation | batch_69f6a0e920cc8190a943fdd0594906c5 |
completed | May 3, 2026, 1:12 a.m. |
Created at: April 29, 2026, 9:17 p.m.