Triple
T6423140
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Triangle Shirtwaist Factory fire |
E127992
|
entity |
| Predicate | majorVictimDemographic |
P36783
|
FINISHED |
| Object | young immigrant women |
—
|
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: young immigrant women | Statement: [Triangle Shirtwaist Factory fire, majorVictimDemographic, young immigrant women]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: majorVictimDemographic Context triple: [Triangle Shirtwaist Factory fire, majorVictimDemographic, young immigrant women]
-
A.
mainVictims
chosen
Indicates that the related entities are the primary or principal targets harmed or affected by an action, event, or perpetrator.
-
B.
victimGroup
Indicates that one group or entity is the target or recipient of harm, abuse, or wrongdoing caused by another.
-
C.
victimAge
Indicates the age of the person who is the victim in the described event or situation.
-
D.
targetedPopulation
Indicates the group of individuals or entities that an action, intervention, or effect is specifically directed toward.
-
E.
demographics
Indicates the relationship of providing or characterizing statistical information about a population’s attributes, such as age, gender, income, or education.
- 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_69c00838de888190af2eec0b80495efa |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0690576c48190b5db5464eacc9de3 |
completed | March 22, 2026, 10:11 p.m. |
| PD | Predicate disambiguation | batch_69c060f780b08190aa650b4d1fc51f21 |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:43 p.m.