Triple
T615937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Warsaw Ghetto Uprising |
E14403
|
entity |
| Predicate | estimatedFighters |
P6153
|
FINISHED |
| Object | several hundred Jewish fighters |
—
|
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: several hundred Jewish fighters | Statement: [Warsaw Ghetto Uprising, estimatedFighters, several hundred Jewish fighters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: estimatedFighters Context triple: [Warsaw Ghetto Uprising, estimatedFighters, several hundred Jewish fighters]
-
A.
combatantStrength
Indicates the relative level of power, capability, or effectiveness one combatant has in a conflict or confrontation compared to others.
-
B.
numberOfInvaders
Indicates the quantity of entities classified as invaders associated with a given subject or context.
-
C.
typeOfTroops
Indicates the specific category or kind of military forces involved in or associated with an entity or event.
-
D.
fleetSize
Indicates the total number of vehicles, vessels, or units that collectively make up a fleet associated with an entity.
-
E.
numberOfTroopsInvolved
chosen
Indicates the quantity of military personnel participating in or assigned to a specific operation, event, or engagement.
- 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_69a4934b17c881909ace8270e8ddd202 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49e0b438881909ad515adf7a4eb79 |
completed | March 1, 2026, 8:14 p.m. |
| PD | Predicate disambiguation | batch_69a49cfbcbf88190a854921dc531eba8 |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:35 p.m.