Triple
T21445832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1968 Ford sewing machinists strike |
E529071
|
entity |
| Predicate | genderOfMainParticipants |
P113101
|
FINISHED |
| Object | female |
—
|
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: female | Statement: [1968 Ford sewing machinists strike, genderOfMainParticipants, female]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: genderOfMainParticipants Context triple: [1968 Ford sewing machinists strike, genderOfMainParticipants, female]
-
A.
genderOfMembers
chosen
Indicates the gender or genders associated with the members of a group or organization.
-
B.
genderDepicted
Indicates that the relationship specifies the gender of the entity as it is represented or portrayed in some context.
-
C.
genderOfResidents
Indicates the gender identity or classification associated with the residents of a particular place or group.
-
D.
hasGenderOfPerson
Indicates that a person is associated with a specific gender classification.
-
E.
genderSpecificity
Indicates whether the relationship or action applies specifically to a particular gender or is gender-neutral.
- 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_69e0c457579481909db68053ed99750c |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e8b707ecd88190b3576b8923840870 |
completed | April 22, 2026, 11:54 a.m. |
| PD | Predicate disambiguation | batch_69e631df1b38819088d3604854e697b4 |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:05 p.m.